CSymPlan: Certified Symbolic Planning and Control for High-DOF Manipulators

• URL: http://arxiv.org/abs/2608.22983v1
• Date: Mon, 24 Aug 2026 08:45:37 GMT
• Title: CSymPlan: Certified Symbolic Planning and Control for High-DOF Manipulators
• Abstract summary: CSymPlan is a symbolic planning and control framework for high-DOF manipulators.<n> offline implementation precomputes certified reach-avoid feedback policies for known workspaces.<n> online implementation synthesizes or updates symbolic policies at runtime from changing task and perception information.<n>Both implementations complete reach-avoid tasks with zero safety violations.
• Score: 4.257148651564725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot manipulators are commonly engineered around a decoupled motion-generation stack: a planner computes a collision-free path and a lower-level controller tracks the resulting reference. This separation is computationally convenient, but it can produce references that are difficult to execute under actuator limits, tracking error, model mismatch, and small obstacle clearances. We present CSymPlan, a certified symbolic planning and control framework for high-DOF manipulators with two complementary implementations: an offline implementation that precomputes certified reach-avoid feedback policies for known workspaces; and an online implementation that synthesizes or updates symbolic policies at runtime from changing task and perception information using parallelization. The offline implementation reduces the manipulator dynamics to a sampled perturbed double-integrator model in operational space through feedback linearization, treats torque-realization errors, modeling inaccuracies, and measurement uncertainty as bounded disturbances, and refines the synthesized symbolic policy to the Franka FR3 through a quantization--lookup--torque realization pipeline. The online implementation uses the same abstraction and refinement interface, but replaces the precomputed policy table with a runtime pFaces request--synthesis--execution loop. In randomized simulated benchmarks and perception-driven Franka FR3 experiments, both implementations complete reach-avoid tasks with zero safety violations; whenever no certified action exists, the robot holds, replans, or stops safely instead of executing an uncertified command.

Related papers

• TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes [65.10128145059127]
  This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models.<n>We propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity.<n>Experiments show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data.
  arXiv  Detail & Related papers  (2026-08-27T03:44:49Z)
• From Detection to Action: Using LLM Agents for Fault-Tolerant Control [0.21313236831585222]
  We propose an agentic Large Language Model (LLM) framework for Fault-Tolerant Control (FTC)<n>It transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge.<n>We show that semantically grounded agents can derive valid recovery decisions within latency budgets compatible with the respective process dynamics.
  arXiv  Detail & Related papers  (2026-06-26T12:13:58Z)
• CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation [61.25264898597319]
  We show that complex manipulation capabilities can emerge naturally from the composition of simple, independent behaviors.<n>We propose CoStream, a framework orchestrating foundation models and diverse sensing modalities into multiple composable core behaviors.<n>We demonstrate CoStream on 8 real-world tasks spanning everyday manipulation and precision assembly, with the strongest gains in contact-rich assembly and object transfer.
  arXiv  Detail & Related papers  (2026-06-24T22:25:19Z)
• Planning-aligned Token Compression for Long-Context Autonomous Driving [95.59023657139208]
  We propose a planning-aligned working memory framework built on conditional VQ-VA.<n>Compression is conditioned on both historical trajectory and a learned planning intent.<n>We evaluate on high-signal dynamic scenarios where historical context is most critical for behavior correctness.
  arXiv  Detail & Related papers  (2026-06-05T17:16:21Z)
• Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
  We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
  arXiv  Detail & Related papers  (2026-05-05T04:49:38Z)
• ContractionPPO: Certified Reinforcement Learning via Differentiable Contraction Layers [5.690649768462432]
  We present ContractionPPO, a framework for robust planning and control of legged robots.<n>ContractionPPO produces a contraction metric that certifies exponential stability of the simulated closed-loop system.<n>Our hardware experiments on quadruped locomotion demonstrate that ContractionPPO enables robust, certifiably stable control even under strong external perturbations.
  arXiv  Detail & Related papers  (2026-03-20T04:32:18Z)
• ParaMaP: Parallel Mapping and Collision-free Motion Planning for Reactive Robot Manipulation [17.260700135265267]
  Real-time and collision-free motion planning remains challenging for robotic manipulation in unknown environments.<n>We propose a parallel mapping and motion planning framework that tightly integrates Euclidean Distance Transform (EDT)-based environment representation with a sampling-based model predictive control (SMPC) planner.<n>The effectiveness of the proposed framework is validated through extensive simulations and real-world experiments on a 7-DoF robotic manipulator.
  arXiv  Detail & Related papers  (2025-12-27T12:24:10Z)
• Constrained Decoding for Robotics Foundation Models [12.916330118607918]
  We introduce SafeDec, a constrained decoding framework for autoregressive robot foundation models.<n>Task-specific safety rules are expressed as Signal Temporal Logic (STL) formulas and are enforced at inference time with minimal overhead.<n>Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining.
  arXiv  Detail & Related papers  (2025-09-01T19:17:40Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.