Verified Task-Space Motion Planning Under Joint-Space Constraints
- URL: http://arxiv.org/abs/2605.22991v1
- Date: Thu, 21 May 2026 19:42:29 GMT
- Title: Verified Task-Space Motion Planning Under Joint-Space Constraints
- Abstract summary: Reactive task-space planners such as Bug2 operate with fixed Cartesian step sizes and are unaware of the manipulator's joint-angle limits.<n>We address this by computing, at each planning step, the largest Cartesian hyperrectangle that is certifiably reachable under joint bounds.<n>An equivalent bisection procedure solves the certification in sub-millisecond time.
- Score: 14.31054942510699
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Reactive task-space planners such as Bug2 operate with fixed Cartesian step sizes and are unaware of the manipulator's joint-angle limits. When the Jacobian is poorly conditioned, even small Cartesian steps can demand joint changes that exceed admissible bounds; clipping the joints to their limits causes tracking drift and can prevent goal reaching entirely. We address this by computing, at each planning step, the largest Cartesian hyperrectangle that is \emph{certifiably reachable} under joint displacement bounds. Using a second-order polynomial approximation of the inverse kinematics and the S-procedure, we formulate a small semidefinite program whose solution yields the certified half-width~$λ^\star$. An equivalent bisection procedure exploiting the quadratic structure solves the certification in sub-millisecond time. Integrating this certificate with Bug2 yields a planner whose step size adapts to local kinematic conditioning. In a statistical evaluation over 94 adversarial scenarios spanning six joint-limit settings, the SOS-verified planner achieves \emph{zero} joint-limit violations with a 100\% goal-reaching rate, whereas a standard Bug2 planner violates joint limits in 6--11\% of steps and fails to reach the goal in up to 18\% of scenarios.
Related papers
- LePlanner: An Iterative Amortized Controller For World Models [0.0]
We propose LePlanner, an amortized iterative controller that learns to construct and refine latent action sequences through a frozen world-model predictor.<n>LePlanner is trained with an arrival-and-hold objective that encourages the controller to reach the goal at the earliest feasible horizon and remain there.<n>It achieves success rates of 98% on PushT, 100% on Reacher, 100% on TwoRooms, and 92% on the OGBench Cube task.
arXiv Detail & Related papers (2026-09-12T10:01:21Z) - Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles [49.315843050082925]
We present a bi approach for minimum-time motion planning convex obstacles that is guaranteed to converge.<n>We find that the proposed method produces high-quality trajectories with times to produce state-of-the-art decomposition of variables.
arXiv Detail & Related papers (2026-08-03T19:48:24Z) - Grasp Execution Without a Planner: Configuration-Space Grasp Distance Fields with Certified Safety & Guaranteed Quality [5.99447754429793]
We present Grasp Distance Fields (GDFs), smooth softmin distances to finite sets of arm-hand grasp configurations.<n>We jointly select and execute grasps without planning a trajectory.<n>A CBF-CLF quadratic program enforces self-collision, workspace, object, and obstacle-clearance constraints.
arXiv Detail & Related papers (2026-08-01T11:35:08Z) - Fitting Unknown Number of Hyperplanes with Manifold Optimization [57.48093263119306]
Fitting unknown number of linearplanes to data is a fundamental yet challenging problem in machine learning.<n>Existing approaches often struggle with optimum optima or lack geometric consistency.
arXiv Detail & Related papers (2026-05-27T14:02:20Z) - Constraint-Enhanced Reinforcement Learning Based on Dynamic Decoupled Spherical Radial Squashing [1.2036262042590717]
Hard limits on how fast each joint can move per control step are unavoidable.<n>Existing methods impose isotropic ball-shaped constraints, exponentially under-covering the true feasible set.<n>This paper proposes Dynamic Decoupled Spherical Radial Squashing (DD-SRad)<n>Experiments with Unitree H1 and G1 humanoid robots confirm end-to-end optimality parameterized directly from official joint specifications.
arXiv Detail & Related papers (2026-05-05T18:24:46Z) - Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
arXiv Detail & Related papers (2026-04-22T22:58:47Z) - Bridging Discrete Planning and Continuous Execution for Redundant Robot [12.278986080239692]
Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators.<n>However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter.<n>This work proposes a bridging framework between discrete planning and continuous execution without modifying the discrete planner itself.
arXiv Detail & Related papers (2026-04-02T13:23:54Z) - Perceptive Variable-Timing Footstep Planning for Humanoid Locomotion on Disconnected Footholds [2.963101656293054]
We propose an onboard, perceptive mixed-integer model predictive control framework that jointly plans foot placement and step duration.<n>We evaluate the approach in simulation on Digit on randomized stepping-stone fields, including external pushes.
arXiv Detail & Related papers (2026-03-08T01:04:30Z) - Reinforcement learning with combinatorial actions for coupled restless bandits [62.89013331120493]
We propose SEQUOIA, an RL algorithm that directly optimize for long-term reward over the feasible action space.<n>We empirically validate SEQUOIA on four novel restless bandit problems with constraints: multiple interventions, path constraints, bipartite matching, and capacity constraints.
arXiv Detail & Related papers (2025-03-01T21:25:21Z) - Improved Pairwise Measurement-Based Surface Code [0.0]
We devise a new realization of the surface code on a rectangular lattice of qubits utilizing single-qubit and nearest-neighbor two-qubit Pauli measurements.
This realization gains substantial advantages over prior pairwise measurement-based realizations of the surface code.
arXiv Detail & Related papers (2023-10-19T17:59:55Z) - Skip-Plan: Procedure Planning in Instructional Videos via Condensed
Action Space Learning [85.84504287685884]
Skip-Plan is a condensed action space learning method for procedure planning in instructional videos.
By skipping uncertain nodes and edges in action chains, we transfer long and complex sequence functions into short but reliable ones.
Our model explores all sorts of reliable sub-relations within an action sequence in the condensed action space.
arXiv Detail & Related papers (2023-10-01T08:02:33Z) - $\texttt{FedBC}$: Calibrating Global and Local Models via Federated
Learning Beyond Consensus [66.62731854746856]
In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information.
In this work, we calibrate this tradeoff in a quantitative manner through a multi-criterion-based optimization.
We demonstrate that $texttFedBC$ balances the global and local model test accuracy metrics across a suite datasets.
arXiv Detail & Related papers (2022-06-22T02:42:04Z) - Balancing Rates and Variance via Adaptive Batch-Size for Stochastic
Optimization Problems [120.21685755278509]
In this work, we seek to balance the fact that attenuating step-size is required for exact convergence with the fact that constant step-size learns faster in time up to an error.
Rather than fixing the minibatch the step-size at the outset, we propose to allow parameters to evolve adaptively.
arXiv Detail & Related papers (2020-07-02T16:02:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.