Library · Optimization & control
What Trajectory & Motion Planning got wrong, from 66 theses
Summary written by a model from the records below: Across the evaluated trajectory and motion planning records, methods frequently break down when physical kinematic constraints, actuator limits, and real-time computation budgets are violated. Planners also suffer from local minima entrapment, overly conservative obstacle representations, uncompensated latency, and poor generalization in unconstrained learned policies. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 8 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Unconstrained neural policies and imitation learning fail to produce reliable trajectory tracking
Pure neural networks, behavioral cloning, and imitation learning frequently suffer from goal divergence, erratic non-smooth motions, and poor generalization across dynamic tasks. These learned representations struggle to capture multi-fidelity dynamics and safety constraints without explicit barrier guidance, reinforcement fine-tuning, or structured trajectory formulations.
Tried and failed
zero-shot vision-language models applied to robot trajectory planning and motion reasoning. Outcome: did not generalise. Reason: models exhibit severe forward-motion and deceleration biases and fail at temporal motion reasoning
Building Intelligence that can Interact with the Physical World · MIT · PhD
Tried and failed
behavior cloning from human demonstrations applied to autonomous ground vehicle goal navigation. Reason: learned obstacle avoidance behaviors but lacked sufficient goal-directed progress, causing consistent timeouts
Towards Improving and Extending Traditional Robot Autonomy with Human Guided Machine Learning · Virginia Tech · Doctorate
Tried and failed
supervised pretraining without reinforcement learning fine-tuning applied to robot trajectory generation and tracking. Outcome: worse than baseline. Reason: supervised learning alone fails to optimize multi-fidelity dynamics and trajectory tracking performance
Multi-fidelity Optimal Trajectory Generation: Optimal Experiment Design for Robot Learning · MIT · PhD
Tried and failed
test-time policy adaptation via action prediction applied to visual robot navigation. Outcome: worse than baseline. Reason: self-supervised action prediction auxiliary objective degraded navigation calibration performance during deployment
Harnessing Synthetic Data for Robust and Reliable Vision · Georgia Tech · PhD
Tried and failed
model-free reinforcement learning for continuous trajectory tracking applied to multirotor flight control. Outcome: worse than baseline. Reason: heading tracking accuracy and latency did not improve over waypoint-stabilization controllers
Deep Reinforcement Learning for Multirotor Flight Control: A Comparative Study of Sim-to-Real Training and Real-World Performance · Virginia Tech · Doctorate
Lost to a baseline
FCNet (pure neural network policy without BarrierNet guidance) achieved lower final robustness and objective value than BarrierNet, getting trapped in sub-optimal obstacle avoidance trajectories
Temporal logic robot control using machine learning · Boston University · PhD
Considered and rejected
Considered and rejected: Training neural network car following models by predicting one step ahead from past empirical trajectories rejected due to poor testing performance compared to unrolled simulation.
Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected direct end-to-end joint-level RL control for human-aware trajectory planning due to high dimensionality, convergence failure, and poor generalization, switching to parametric Cartesian path generation.
Effective and safe framework for human-robot interaction · Politecnico di Torino · PhD
Tried and failed
second-order dynamical system imitation learning applied to robot trajectory generation. Outcome: did not generalise. Reason: unaligned initial velocities cause severe trajectory deviations without directional dissipation
Geometric Learning: Leveraging differential geometry for learning and control · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected conditioning policies on goal images or continuous trajectory sketches due to over-specification, high input dimensionality, and lack of orientation context.
Scaling robot learning with heterogeneous data from the real world, simulation, and the web · UT Austin · PhD
Tried and failed
embedding neural networks directly in quadratic programs applied to real-time robot trajectory optimization. Outcome: worse than baseline. Reason: frequent optimization failures compared to using Gaussian processes inside the quadratic program
Advancing the Adaptability of Compliant Robot Controllers for Exploration, Interaction, and Manipulation · EPFL · PhD
Tried and failed
joint multi-waypoint policy optimization with RL applied to trajectory synthesis for robotic manipulation. Outcome: unstable. Reason: optimizing all trajectory waypoints simultaneously caused performance degradation over training time
Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation · Virginia Tech · Master's
Tried and failed
Taylor series expansion of neural network cost applied to iterative linear quadratic regulator trajectory optimization. Outcome: worse than baseline. Reason: local quadratic approximations of deep discriminators are less effective than explicitly structured quadratic compositions
Imitation learning from observation · UT Austin · PhD
Tried and failed
probabilistic movement primitives from full trajectory demonstrations applied to dynamic robot handover tasks. Outcome: unstable. Reason: generated abrupt, non-smooth motions causing visual tracking failure on wrist cameras
A Stochastic Approach to Contact-rich Manipulation · EPFL · PhD
Tried and failed
joint optimization of control barrier functions and policy applied to safe robot navigation with dynamic obstacles. Outcome: worse than baseline. Reason: inability to handle moving targets leading to high collision rates
Personalized, Safe, and Interactive Robot Programming via Human Demonstrations · Georgia Tech · PhD
Neglecting kinematics and dynamic actuator constraints yields physically infeasible or unstable trajectories
Trajectory optimizers that omit joint torque bounds, terrain resistance, or subsystem kinematics generate paths that physical robotic platforms cannot execute. These unconstrained formulations cause primary joint drift, whiplash instabilities, extreme energy losses, and mathematical singularities during aggressive maneuvers.
Tried and failed
greedy viewpoint selection without kinematic constraints applied to autonomous robotic coverage path planning. Outcome: worse than baseline. Reason: ignoring reachability and velocity bounds generates dynamically infeasible trajectories, reducing actual coverage
Enhancing viewpoint planning and time-optimal trajectory generation for autonomous robot surveying · UT Austin · PhD
Tried and failed
shortest distance path planning applied to off-road vehicle trajectory optimization. Outcome: worse than baseline. Reason: minimizing geometric distance ignores terrain motion resistance, causing highest energy loss and lowest tractive efficiency
Evaluation of off-road uninhabited ground vehicle mobility using discrete element method and scalability investigation · Cranfield · PhD
Tried and failed
trajectory optimization with non-zero initial boundary curvature applied to undulatory locomotion gait generation. Outcome: worse than baseline. Reason: enforced non-zero boundary curvature produced energetically suboptimal intermediate S-shaped body configurations
Gait optimality for undulatory locomotion with applications to C. elegans phenotyping · Imperial · PhD
Tried and failed
trajectory optimization assuming lower-tier subsystem kinematics applied to multi-joint prosthetic limb design. Outcome: did not generalise. Reason: optimizing for distal kinematics caused kinetic errors and asymmetric gait when knee joint dynamics were missing
Integrated Prosthetic Leg Design Frameworks for People with an Above-Knee Amputation · MIT · PhD
Tried and failed
centroidal dynamics with full kinematics trajectory optimization applied to humanoid dynamic jumping maneuvers. Reason: omits joint torque and electrical power limits, yielding physically infeasible trajectories
A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion · MIT · PhD
Tried and failed
trajectory optimization with underconstrained proximal joints applied to multibody dynamic motion prediction. Reason: omitting realistic dynamic torso and shoulder rotation constraints caused downstream distal trajectory prediction errors
Optimization-Based Motion Prediction for Tossing Tasks · Texas Tech · PhD
Considered and rejected
Considered and rejected: Rejected directly optimizing feature vectors followed by trajectory reconstruction in motion attribution because target feature vectors often have no physically realizable trajectory satisfying domain constraints.
Making Robot Behaviors Automatically Transparent · University of Washington · PhD
Tried and failed
differential flatness mapping applied to aggressive quadrotor trajectory generation. Reason: mapping singularities near 90-degree tilts from hover restrict aggressive acceleration and pitch/roll trajectories
Trajectory Planning for Flights in Multiagent and Dynamic Environments · MIT · PhD
Tried and failed
Kinematic control without null-space optimization applied to Redundant manipulator trajectory tracking. Reason: Unmanaged redundancy caused primary joints to drift and exceed physical joint limits during continuous motion.
Robotic manipulation of hand tools: estimation, planning, control and finger gaiting · Iowa State · PhD
Tried and failed
model predictive control with order-reduced linearized models applied to dynamic visual trajectory tracking. Outcome: did not generalise. Reason: rapid operating-point velocity changes and cueing time delays degraded linear model validity
Model-Based Life Extending Control for Rotorcraft · Georgia Tech · PhD
Non-convex optimization landscapes and local minima trap trajectory planners
Planners relying on gradient descent, penalty methods, or local sampling easily get trapped in suboptimal critical points when navigating tight obstacle configurations. Relying on semi-analytic guesses or local velocity obstacles without global lookahead frequently triggers optimization divergence and failure to satisfy intermediate constraints.
Tried and failed
semi-analytic initial guess for trajectory optimization applied to low-thrust orbit insertion trajectory design. Outcome: did not converge. Reason: insufficient control flexibility per revolution combined with high-dimensional nonlinear programming design space
A Shape Based Approach for Preliminary Design of Low Thrust Space Trajectories · Iowa State · PhD
Tried and failed
sampling-based model predictive control applied to high-dimensional trajectory planning around obstacles. Outcome: did not converge. Reason: Unimodal trajectory sampling became trapped in sub-optimal local minima between obstacles in high-dimensional spaces
Of Priors and Particles: Structured and Distributed Approaches to Robot Perception and Control · Georgia Tech · PhD
Tried and failed
dynamic movement primitives under waypoint constraints applied to robot trajectory generation with obstacle avoidance. Outcome: did not converge. Reason: hyperparameters failed to reliably find trajectories satisfying both intermediate via-point and obstacle avoidance constraints
Automatically Encoding, Modifying, and Finding Robot Skills to Repair High-Level Tasks · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected deterministic variational least-squares trajectory recovery due to ill-posedness and high-dimensional multi-modal optimization landscapes caused by agent symmetry
MULTI-AGENT SYSTEMS: OBSERVABILITY, CLASSIFICATION AND CLUSTERING PREDICTION · Johns Hopkins · PhD
Tried and failed
direct parameter sampling for trajectory optimization applied to articulated robotic manipulation planning. Outcome: did not converge. Reason: sampling planners frequently terminated prematurely returning incomplete approximate solutions instead of valid paths
Remote robotic manipulation task execution using affordance primitives · UT Austin · PhD
Tried and failed
quasi-Newton local optimization applied to non-convex trajectory and partition optimization. Reason: Local minimization got trapped in suboptimal critical points instead of reaching the global minimum.
PERIMETER DEFENSE WITH MULTI-ROBOT SYSTEMS · Penn · PhD
Tried and failed
local velocity obstacle trajectory planning applied to multi-agent obstacle avoidance. Outcome: did not converge. Reason: Local optimization gets trapped in local minima formed by static obstacles without global path lookahead
Conceptual-Level Analysis and Design of Unmanned Air Traffic Management Systems · Georgia Tech · PhD
Tried and failed
sequential path stepping gradient descent optimization applied to constrained robotic trajectory planning. Outcome: did not converge. Reason: algorithm failed in environments with tight obstacles and long paths
Remote robotic manipulation task execution using affordance primitives · UT Austin · PhD
Tried and failed
naive penalty differential dynamic programming applied to constrained non-convex trajectory optimization. Outcome: did not converge. Reason: indefinite Hessian terms in non-convex obstacle fields and tight environments cause local minima or divergence
Safety Embedded Optimal Decision Making and Control Via Barrier States · Georgia Tech · PhD
Tried and failed
semi-analytic initial guesses for high-revolution trajectory optimization applied to many-revolution low-thrust trajectory optimization. Outcome: did not converge. Reason: linearized/semi-analytic approximations accumulated too much error over many revolutions for the numerical optimizer to converge
A Shape Based Approach for Preliminary Design of Low Thrust Space Trajectories · Iowa State · PhD
Lost to a baseline
On the CSAIL dataset, DC-SAM attained higher trajectory error than graduated nonconvexity (GNC) due to entrapment in local optima.
Lifelong, learning-augmented robot navigation · Woods Hole · PhD
Considered and rejected
Considered and rejected: Rejected nonlinear trajectory optimization due to heavy initialization dependence and lack of convergence guarantees
An Optimization Approach to Certified Manipulation · MIT · PhD
High dimensional parameterizations cause exponential computational intractability
Expanding search dimensionality through dense forward sampling, uniform mesh refinement, or joint waypoint and time allocation severely degrades solver efficiency. Techniques such as dynamic programming, zonotope reachability, and scenario-tree predictive control quickly become computationally intractable for real-time trajectory generation.
Tried and failed
joint optimization of boundary points and time allocations applied to multi-segment trajectory planning. Outcome: worse than baseline. Reason: tripling optimization dimensionality degraded convergence and solution quality for fixed iteration budgets
Multi-fidelity Optimal Trajectory Generation: Optimal Experiment Design for Robot Learning · MIT · PhD
Tried and failed
increasing parameterization dimensionality in evolutionary trajectory optimization applied to continuous path planning in vector fields. Outcome: worse than baseline. Reason: Expanded search space degraded genetic algorithm optimization efficiency compared to lower-dimensional representations.
EXPLOITING FLOWS FOR ORIENTEERING AND PLANNING PROBLEMS · Penn · PhD
Considered and rejected
Considered and rejected: Rejected dynamic programming due to the curse of dimensionality on minimum-time trajectory problems.
Artificial Drivers for Online Time-Optimal Vehicle Trajectory Planning and Control · University of Trento · PhD
Considered and rejected
Considered and rejected: Rejected full dynamic programming / Q-table pre-computation for multi-task navigation due to excessive real-time human cognitive load and curse of dimensionality.
A modular attention hypothesis for modeling visuomotor behaviors · UT Austin · PhD
Tried and failed
coupled zonotope reachability model predictive control applied to real-time robot trajectory planning. Outcome: too slow. Reason: computational complexity was two orders of magnitude too high to meet real-time update frequency requirements
Safe Bipedal Locomotion and Navigation in Uncertain Environments · Georgia Tech · PhD
Tried and failed
scenario-tree min-max model predictive control applied to real-time legged robot trajectory planning. Outcome: too slow. Reason: exponential growth of uncertainty realizations across the planning horizon made real-time optimization computationally intractable
Robust Predictive Control for Legged Locomotion · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected sampling-based trajectory planners (MCTS/RRT) due to prohibitive computational expense when repeatedly evaluating counterfactual belief states.
Enabling Human-Multi-Robot Collaborative Visual Exploration in Underwater Environments · MIT · PhD
Considered and rejected
Considered and rejected: Rejected direct non-iterative dense forward sampling (M >= 100,000) due to prohibitive compute when the optimal trajectory distribution is initially unknown.
Feynman-Kac Numerical Techniques for Stochastic Optimal Control · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected distributionally robust optimization (DRO) and brute-force search for trajectory planning due to severe computational complexity and overly conservative trajectories.
Dynamic Connected Automated Vehicle Trajectory and Traffic Signal Timing Optimization · Virginia Tech · Doctorate
Tried and failed
gradient descent trajectory optimization applied to orbital maneuver estimation. Outcome: too slow. Reason: excessive runtime from repeated forward propagations and inability to directly provide uncertainty quantification
Applications of random finite set-based multi-target trackers in space situational awareness · UT Austin · PhD
Tracking controllers destabilize under dynamic model mismatch and uncooperative interactions
Trajectory tracking baselines and game-theoretic planners degrade when encountering unmodeled environmental disturbances, adaptive oscillations, or uncooperative agents. Inaccurate assumptions such as constant velocity or zero lead-vehicle acceleration induce severe limit cycles, tracking divergence, and degraded safety margins.
Tried and failed
deterministic dynamic game planning without uncertainty tracking applied to multi-agent interactive robot navigation. Outcome: worse than baseline. Reason: failed to account for motion noise and heterogeneous observation uncertainty during aggressive maneuvers
Learning and control for interactions in mixed human-robot environments · MIT · PhD
Lost to a baseline
Robot-only handover planner achieved a 62% success rate on randomized obstacle trajectories compared to 57% for the collaborative MPC optimizer when partners were uncooperative
The Expert is the Obstacle: Building a General Framework for Learned Robot Motion · University of Washington · PhD
Lost to a baseline
MBDRL was outperformed in navigation time by an aggressively tuned PID in single-drone navigation (though PID suffered from overshooting).
Cooperative Payload Transportation by UAVs: A Model-Based Deep Reinforcement Learning (MBDRL) Application · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected estimating MPC lead vehicle trajectory assuming zero acceleration, because inaccurate prediction degrades follower string stability and causes lagging/overshooting responses
Longitudinal Control for Self-driving Cars with Traffic Flow Considerations: Theory, Design, and Experiments · Georgia Tech · PhD
Lost to a baseline
Hand-tuned or standard LQR/purely robust controllers under small/zero uncertainty match nominal trajectory, but fail to navigate or require excessive control effort when uncertainty grows
Online data-driven control of safety-critical systems · Boston University · PhD
Tried and failed
minimum cumulative jerk trajectory timing control applied to robot reach-to-handover motion generation. Outcome: worse than baseline. Reason: caused higher optimization infeasibility rates and poorer user experience than proportional velocity control
Robot Controllers, Gaze Behaviors and Human Motion Datasets for Object Handovers · Cornell · PhD
Tried and failed
proportional myoelectric control applied to powered lower-limb robotic exoskeleton assistance. Outcome: worse than baseline. Reason: Users preferred consistent robotic assistance trajectories over biologically proportional torque control based on muscle activity
Enhancing Human Locomotion using a Robotic Knee Exoskeleton · Georgia Tech · PhD
Lost to a baseline
Two mobile beacons in fixed formation (2M.F) achieved lower trajectory error than the optimal placement method (2M.O) in some individual simulation runs when abrupt AUV turns invalidated constant-velocity predictions
Cooperative Multi-Robot Systems for Aquatic Environmental Sensing · EPFL · PhD
Tried and failed
reproducing kernel hilbert space adaptive control applied to autonomous underwater vehicle trajectory tracking. Outcome: unstable. Reason: baseline non-parametric adaptive control exhibited steady-state limit cycle oscillations in pitch and depth
Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles · Virginia Tech · Master's
Lost to a baseline
Weighted pseudoinverse and GUAM baseline diverged and exceeded trajectory tracking error bounds (±2 m/s / ±2 deg/s) during transition-to-cruise switch under n1 and delta_aL failure.
Simulation and Flight-Test Evaluation of Fault-Tolerant Control Allocation Strategies for eVTOL Aircraft · Virginia Tech · Master's
Overly conservative obstacle boundaries eliminate feasible trajectory solutions
Artificially inflating obstacle geometry, over-approximating swept volumes, and pruning goal-diverging rollouts unnecessarily restrict vehicle maneuverability. This excessive conservatism produces inefficient detours, causes premature navigation termination, and eliminates valid paths through cluttered environments.
Tried and failed
obstacle avoidance without proximity scaling applied to dynamic robot trajectory planning. Outcome: worse than baseline. Reason: omitting proximity scaling caused premature, overly conservative avoidance and unnecessary trajectory detours
Tried and failed
velocity obstacles with large planning horizons applied to robot navigation in dynamic crowds. Outcome: worse than baseline. Reason: over-constraining constant acceleration assumptions caused excessive collisions and inefficiency
Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices · EPFL · PhD
Tried and failed
Goal-proximity rollout pruning for dynamic obstacle prediction applied to multi-agent collision avoidance trajectory planning. Outcome: worse than baseline. Reason: Avoiding congestion often requires agents to temporarily move away from their destinations, which pruned valid paths.
Applying FastMDP to complex aerospace-related problems · Iowa State · PhD
Tried and failed
bidirectional sampling-based motion planning applied to manipulation trajectory planning in dense clutter. Reason: Cannot find paths when strictly collision-free trajectories do not exist, triggering destructive fallback behaviors
Robotic manipulation in clutter with object-level semantic mapping · Imperial · PhD
Tried and failed
stochastic reachability analysis with high risk threshold applied to collision avoidance trajectory planning. Reason: overly restrictive probability threshold truncated reachable sets, omitting critical obstacle occupancy states
Threat Assessment and Proactive Decision-Making for Crash Avoidance in Autonomous Vehicles · Virginia Tech · Master's
Tried and failed
swept volume over-approximation for collision avoidance applied to trajectory and manipulator structure synthesis. Reason: over-approximation was too conservative, eliminating feasible paths in constrained obstacle navigation tasks
Task-Based Design Synthesis Of Modular Manipulators · Cornell · PhD
Open-loop execution and uncompensated latency cause severe tracking drift
Executing trajectory priors or action sequences without closed-loop state tracking controllers causes severe deviations from intended paths. Computation delays leave initial planning states outdated, while uncorrected sensor integration errors accumulate into unrecoverable position lag and pilot oscillations.
Tried and failed
direct execution of diffusion-generated actions applied to robot trajectory control. Outcome: worse than baseline. Reason: generated action sequences execute poorly directly, requiring state-to-action tracking controllers to function reliably
Video as the Language of Embodied Intelligence · MIT · PhD
Considered and rejected
Considered and rejected: Rejected precomputing global reference trajectories through all waypoints beforehand due to online infeasibility when obstacle avoidance is introduced
Receding horizon trajectory optimization through waypoints and path segments · TU Wien · PhD
Tried and failed
Direct open-loop execution of extracted trajectory priors applied to robotic grasping and manipulation tasks. Outcome: did not generalise. Reason: Demonstration trajectories without interactive exploration cannot handle real-world physical and kinematic discrepancies.
Teaching Robots using Interactive Imitation Learning · Virginia Tech · Doctorate
Tried and failed
dead-reckoning inertial navigation applied to underwater vehicle localization. Outcome: unstable. Reason: unbounded integration error caused by sensor drift over time
Human-Interactions with Robotic Cyber-Physical Systems (CPS) for Facilitating Construction Progress Monitoring · Virginia Tech · Doctorate
Tried and failed
open-loop waypoint passing without delay compensation applied to real-time vehicle trajectory planning. Outcome: unstable. Reason: Vehicle movement during planning computation rendered initial pose outdated, causing erratic trajectory execution
Terrain aware tactical motion planning and control algorithms for off-road UGVs in GNSS denied hostile environments · Virginia Tech · Doctorate
Tried and failed
egocentric visual speed guidance cueing applied to manual closed-loop vehicle trajectory tracking. Outcome: unstable. Reason: velocity tracking errors integrated into unrecoverable position lag, driving pilot-induced oscillations in low visibility
Augmented reality cueing methodologies for rotorcraft shipboard landings · Georgia Tech · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Benchmark DDPG, TD3, PPO, DDP, collocation, and MPPI against SAC for learning maneuver automaton motion primitives in navigation simulations. Blocker: None
Autonomous Methods for Learning and Pruning Motion Primitives for Navigation and Adversarial Tasks · Georgia Tech · PhD
Left open
Train machine learning models (Random Forest, SVM, Deep Learning) on LiDAR trajectory data to classify driver behavior instead of rule-based kinematic thresholds. Blocker: Access to the thesis author's roadside LiDAR trajectory dataset and proprietary VISSIM simulation environment.
Calibration of Microscopic Traffic Simulation Models for Proactive Safety Performance based on LiDAR Trajectory Data · Texas Tech · PhD
Left open
Extend adversarial trajectory attack methods to incorporate collision velocity and group behaviors for autonomous driving motion forecasting models. Blocker: None
Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis · EPFL · PhD
Left open
Extend perception-aware view optimization and trajectory planning to mobile vehicles equipped with robotic manipulators in simulation. Blocker: None
Perception-aware planning for differentially flat robots · MIT · PhD
Left open
Integrate symbolic task-level planning with low-level continuous motor trajectory optimization and reactive re-planning. Blocker: The goal is a high-level research direction without concrete benchmarks, architectures, or targets specified.
Learning to act with objects, relations and physics · MIT · PhD
Left open
Design non-linear interpolation paths for flow matching to incorporate non-Euclidean geometries or trajectory constraints. Blocker: High ambiguity regarding specific manifolds, constraint types, and concrete mathematical formulations to test
Generative modeling by interpolating between distributions · UT Austin · PhD
Left open
Extend cross-learning to navigation problems featuring complex dynamics from human-machine interaction. Blocker: Vague task direction lacking specific formulation, targets, or interactive dynamics models
Graph Machine Learning Under Requirements · Penn · PhD
Left open
Apply the DMD/EDMD Koopman framework to motion capture trajectories of bipedal animals such as kangaroos and ostriches. Blocker: Requires specialized motion capture data or live animal laboratory facilities for kangaroos and ostriches.
Animal Motion Analysis and Approximation for Robotics · Virginia Tech · Master's
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Gilhyun Ryou · MIT · PhD · 2024
Multi-fidelity Optimal Trajectory Generation: Optimal Experiment Design for Robot Learning - Madhusudan Vijayakumar · Iowa State · PhD · 2024
A Shape Based Approach for Preliminary Design of Low Thrust Space Trajectories - MICHELE POLITO · Politecnico di Torino · PhD · 2026
Effective and safe framework for human-robot interaction - Garrett Dale Asper · Virginia Tech · Master's · 2026
Simulation and Flight-Test Evaluation of Fault-Tolerant Control Allocation Strategies for eVTOL Aircraft - Florian Beck · TU Wien · PhD · 2026
Receding horizon trajectory optimization through waypoints and path segments - Boyuan Chen · MIT · PhD · 2025
Video as the Language of Embodied Intelligence - Tsun-Hsuan (Johnson) Wang · MIT · PhD · 2025
Building Intelligence that can Interact with the Physical World - Nina T. Petelina · MIT · PhD · 2025
Integrated Prosthetic Leg Design Frameworks for People with an Above-Knee Amputation - Matthew T. Chignoli · MIT · PhD · 2025
A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion - Letian Zac Chen · Georgia Tech · PhD · 2025
Personalized, Safe, and Interactive Robot Programming via Human Demonstrations - Xiaotong Zhang · MIT · PhD · 2025
thesis in the field of Mechanical Engineering: Relevance for Human-Robot Collaboration: Definitions, Systems, Algorithms, and Applications - Patrick James Thomas · Virginia Tech · Doctorate · 2025
Deep Reinforcement Learning for Multirotor Flight Control: A Comparative Study of Sim-to-Real Training and Real-World Performance
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