Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots

• URL: http://arxiv.org/abs/2207.01418v2
• Date: Tue, 5 Jul 2022 15:26:48 GMT
• Title: Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
• Authors: Yuki Shirai, Xuan Lin, Alexander Schperberg, Yusuke Tanaka, Hayato Kato, Varit Vichathorn, Dennis Hong
• Abstract summary: We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
• Score: 60.06216976204385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To accelerate the planning process, we propose distributed optimization frameworks based on Alternating Direction Methods of Multipliers (ADMM) to solve the original large-scale Mixed-Integer NonLinear Programming (MINLP). The resulting frameworks use Mixed-Integer Quadratic Programming (MIQP) to solve contact and NonLinear Programming (NLP) to solve nonlinear dynamics, which are more computationally tractable and less sensitive to parameters. Also, we explicitly enforce patch contact constraints from limit surfaces with micro-spine grippers. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45{\deg} with a much shorter planning time.

Related papers

• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories. The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles. The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance. We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model [13.110235244912474]
  Redundant manipulators offer enhanced kinematic performance and versatility. Motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
  arXiv  Detail & Related papers  (2024-12-27T07:34:54Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences [20.879194337982803]
  Multi-Agent Path-Finding (MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous guarantees. We propose an approach for accelerating conflict-based search algorithms by leveraging their repetitive and incremental nature.
  arXiv  Detail & Related papers  (2024-03-29T20:31:07Z)
• Neural Potential Field for Obstacle-Aware Local Motion Planning [46.42871544295734]
  We propose a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. Our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
  arXiv  Detail & Related papers  (2023-10-25T05:00:21Z)
• AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation [50.737355245505334]
  We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks. The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation.
  arXiv  Detail & Related papers  (2023-05-30T09:54:20Z)
• Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks [29.239926645660823]
  This paper introduces a novel learning-to-plan framework that exploits the concept of constraint manifold. Our approach generates plans satisfying an arbitrary set of constraints and computes them in a short constant time, namely the inference time of a neural network. We validate our approach on two simulated tasks and in a demanding real-world scenario, where we use a Kuka LBR Iiwa 14 robotic arm to perform the hitting movement in robotic Air Hockey.
  arXiv  Detail & Related papers  (2023-01-11T06:54:11Z)
• Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation [13.65857209749568]
  We demonstrate a new purely distributed technique based on a generic factor graph defining dynamics and collision constraints. We show that our method allows extremely high performance collaborative planning in a simulated road traffic scenario.
  arXiv  Detail & Related papers  (2022-03-22T11:13:36Z)
• Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients [122.85280150421175]
  We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. We employ a graph neural network (GNN) to parameterize policies for the robots.
  arXiv  Detail & Related papers  (2021-02-11T21:57:43Z)

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.