aiqbench analysis
This research improves real-time teleoperation by replacing spherical approximations with capsule and polytope models for higher geometric accuracy. By leveraging duality in convex optimization, the method reduces computation times while preventing self-collisions and environmental impacts on robotic manipulators. The approach demonstrates superior performance in both simulation and real-world tests on a UR5e arm, offering a practical solution for safer remote control systems.
Real-Time and Accurate Collision-Free Teleoperation via Differentiable Constraint-Based Trajectory Planning
arXiv:2606.08725v1 Announce Type: new Abstract: In teleoperation, the human operator typically controls only the end-effector pose, which often leads to self-collisions of the manipulator and collisions with environmental obstacles, since joints and links are not controlled individually. A common strategy to mitigate this issue is to enhance the operator's input using optimal-control-based trajectory planning. As derivative-based solvers require differentiable constraints, existing approaches either approximate robots and obstacles with spheres, reducing geometric accuracy, or approximate derivatives, degrading convergence and increasing computation times. We address these limitations by adapting a recent formulation of differentiable collision-avoidance constraints, based on duality in convex optimization, to the teleoperation setting. The robot is approximated with capsules and the environment with polytopes. We compare the resulting trajectory planning method against state-of-the-art techniques in simulation with varying numbers of obstacles and evaluate it on a UR5e manipulator in a real-world teleoperation test. Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling, leading to smoother and collision-free end-effector teleoperation.
Abstract:In teleoperation, the human operator typically controls only the end-effector pose, which often leads to self-collisions of the manipulator and collisions with environmental obstacles, since joints and links are not controlled individually. A common strategy to mitigate this issue is to enhance the operator's input using optimal-control-based trajectory planning. As derivative-based solvers require differentiable constraints, existing approaches either approximate robots and obstacles with spheres, reducing geometric accuracy, or approximate derivatives, degrading convergence and increasing computation times. We address these limitations by adapting a recent formulation of differentiable collision-avoidance constraints, based on duality in convex optimization, to the teleoperation setting. The robot is approximated with capsules and the environment with polytopes. We compare the resulting trajectory planning method against state-of-the-art techniques in simulation with varying numbers of obstacles and evaluate it on a UR5e manipulator in a real-world teleoperation test. Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling, leading to smoother and collision-free end-effector teleoperation.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2606.08725 [cs.RO]
(or arXiv:2606.08725v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.08725 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 258 words
Related coverage
GPU-Accelerated Polygonal Signed Distance Functions for Real-Time Collision Avoidance
arXiv · Sep 30, 2026
Real-Time Whole-Body Safe Motion Generation for Multi-Segment Tendon-Driven Continuum Robots
arXiv · Oct 2, 2026
Links #3: 2026/06 Part 1
LessWrong · Jun 15, 2026

Building a Foundation Stack for General-Purpose Robots
IEEE Spectrum · Jul 13, 2026