This paper is about generating motion plans for high degree-of-freedom systems that account for both static and dynamic collisions along the entire body. A particular class of mathematical programs with complementarity constraints become useful in this regard. Optimization-based planners can tackle confined space trajectory planning while being cognizant of robot and (mostly static) obstacle constraints. However, handling moving obstacles is non-trivial in a real-time setting. To this end, we present the FLIQC (Fast LInear Quadratic Complementarity based) motion planner. Our reactive planner employs a novel motion model that captures the entire rigid robot as well as the obstacle geometry and ensures nonpenetration between the surfaces due to the imposed constraint. We perform thorough comparative studies with the state-of-the-art, which demonstrate improved performance. Extensive simulation and hardware experiments validate our claim of generating continuous and real-time motion plans at 1 kHz for modern collaborative robots with constant minimal parameters.

Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and adaptive grasp transitions remains a challenge, particularly when dealing with external forces and complex motion constraints. Existing grasp transition strategies often fail to account for varying external forces and do not optimize motion performance effectively. In this work, we propose an Imitation-Guided Bimanual Planning Framework that integrates efficient grasp transition strategies and motion performance optimization to enhance stability and dexterity in robotic manipulation. Our approach introduces Strategies for Sampling Stable Intersections in Grasp Manifolds for seamless transitions between uni-manual and bi-manual grasps, reducing computational costs and regrasping inefficiencies. Additionally, a Hierarchical Dual-Stage Motion Architecture combines an Imitation Learning-based Global Path Generator with a Quadratic Programming-driven Local Planner to ensure real-time motion feasibility, obstacle avoidance, and superior manipulability. The proposed method is evaluated through a series of force-intensive tasks, demonstrating significant improvements in grasp transition efficiency and motion performance. A video demonstrating our simulation results can be viewed at https://youtu.be/3DhbUsv4eDo.

This paper investigates the problem of effective tool manipulation for motion planning in complex human-like scenarios. Vector-field-based real-time strategies, although widely used, usually do not account for unwieldy tools or incorporate systematic methods to handle these extra maneuvers needed. Instead, we formalize the problem and propose a novel field-based reactive planner that explicitly accounts for rotational forces for seamless maneuvers based on the tool’s geometry and featured points. Furthermore, we capture and encode robot performance through capability metrics and improve the same using an additional quality distribution method. This enables seamless integration of the robot’s embodiment with the reactive force-torque (wrench) field giving rise to flexible tool usage in non-stationary environments. Extensive simulation analysis on a 7 DoF collaborative robot manipulating a common tool in an unorganized table-top layout reinforces our claim of robustness in stationary and non-stationary scenarios.