Open Problems

Robot Learning & Embodied Control

Closed-Loop Trajectory Generation for Robot Manipulation Without External Motion Planners

Barrier to removePartly addressed
Possible candidate · 2/5 runs4 papers report this100% from 2025+

Generated automatically from the limitations stated in 4 papers (CVPR, ICLR, NeurIPS), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Current keyframe-based robot manipulation policies predict static target end-effector poses and delegate trajectory generation to external sampling-based motion planners such as BiRRT. This decouples visual perception from execution dynamics, preventing real-time reactive corrections when environments change or objects slip mid-motion. Furthermore, relying on external planners introduces high computational latency and frequent planning failures in cluttered scenes where feasible kinematic paths are narrow.

Why it matters

Manipulation policies that continuously adapt their full arm trajectories to dynamic disturbances and moving objects in real time, eliminating the runtime latency and failure modes of external sampling-based planners.

Ways to approach it

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  1. 1

    Train a diffusion policy or action-chunking policy directly on continuous joint-space or Cartesian trajectory demonstrations in simulation (e.g., Robomimic or RLBench), measuring task success rate, execution time, and cycle-to-cycle replanning frequency under dynamic obstacle perturbations.

  2. 2

    Formulate a hybrid policy that predicts dense waypoints alongside local collision-avoidance potential fields, evaluated against MoveIt/BiRRT baselines on trajectory smoothness, planning latency, and collision rates across cluttered tabletop manipulation environments.

  3. 3

    Distill pre-computed BiRRT and trajectory optimization trajectories into a closed-loop reactive policy operating at 20–50 Hz from visual observations, measuring real-time tracking error and recovery rates under sudden target displacements.

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Why it might fail

High-frequency visuomotor policies (e.g., standard Diffusion Policy or ACT) may already solve this for standard tabletop setups, leaving the open challenge restricted strictly to high-dimensional obstacle avoidance where motion planners maintain formal safety and collision-free guarantees that learned policies cannot yet match.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.