#Motion Planning (12)

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RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulators in Cluttered Environments

RoboNav-Arm lets an LLM decision agent orchestrate scene understanding, contextual memory and trajectory planning modules, adaptively selecting RRT-family planners with safety-inflated collision validation; collision-free success rates range from 100% to 83.3% across four clutter scenarios.

Aachal Sharma, Narendra Kumar Dhar · 501 views · Jun 25, 2026

Robotic ArmObstacle AvoidanceMotion PlanningJun 25, 2026

1:13

OMY + Gemini + GraspNet: VLM-Driven Smart Pick-and-Place

@passionvirus · 756 views · 2026-08-28

VLMROS2OMY

3:10

AdaPT brings professional tennis styles to humanoids

@TaouHuang · 491 views · 2026-08-21

HumanoidMotion PlanningMotion Tracking

HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning

HITTER couples a model-based planner (ball trajectory prediction, strike position/velocity/timing) with an RL whole-body controller trained on human forehand/backhand motion references, enabling a Unitree G1 to sustain rallies of up to 106 consecutive shots against a human opponent.

Zhi Su, Bike Zhang, Nima Rahmanian · 468 views · Aug 28, 2025

HumanoidTable TennisWhole-Body ControlAug 28, 2025

SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

SparseDrive unifies detection, tracking, online mapping, prediction, and planning with sparse scene representation, using a symmetric perception module, a parallel motion planner, and collision-aware rescoring for safe planning.

Wenchao Sun, Xuewu Lin, Yining Shi · 514 views · May 30, 2024

BEVSparseDrivenuScenesMay 30, 2024

Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics

Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nominal model or simulator, which may be invalidated when the true dynamics are unknown or difficult to model accurately. This letter addresses this limitation for systems with unknown dynamics and an available approximate nominal model, contributing a planner-agnostic constraint-tightening procedure that equips existing planners with a probabilistic task-completion guarantee on the true system. We leverage conformal prediction to provide a probabilistic bound on the nominal-to-true trajectory deviation over a distribution of planning problems. We tighten the planning constraints using that bound, and show that solving the tightened problem under the nominal model is a sufficient condition for solving the original problem on the true system with a prescribed probability. We validate the theoretical guarantees empirically and demonstrate substantially improved task completion relative to nominal-model planning.

Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros · 289 views · Jul 24, 2026

Motion PlanningConstraintUnknown DynamicsJul 24, 2026