AI & ML
Motion Generation With Environmental Constraints
arXiv cs.RO0 views
arXiv:2607.25053v2 Announce Type: replace Abstract: Robot motion planning faces challenges in high-dimensional spaces and uncertain environments, often constrained by the need for collision-free motions. We advocate an alternative approach, Environmental Constraint Exploitation (ECE), where deliberate contact with the environment simplifies planning by reducing dimensionality and computational complexity. By integrating ECE into motion planning algorithms, we bias exploration to task-relevant regions and leverage contact for uncertainty reduction to improve robustness during execution. We evaluate ECE benefits with RRT-based planners and demonstrate their practical benefits in a real-world application. This work consolidates and extends prior research, showcasing how ECE simplifies motion planning while enhancing adaptability and performance in complex environments.
Source:https://arxiv.org/abs/2607.25053PreviousSemantic Anchoring for Robotic Action RepresentationsNextRAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution
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