This work proposes a fast bimanual motion planning pipeline that enforces this hard transformation constraint continuously along the entire path, using a leader-follower parameterization: the leader's configuration is treated as a free variable, while the follower's is determined via inverse kinematics to satisfy the constraint.
Abstract
Bimanual manipulation enables complex tasks but introduces added complexity from the high number of degrees of freedom involved. When handling rigid objects, the relative transformation between the two end effectors must remain fixed throughout the motion, manifesting as a nonlinear equality constraint that confines the feasible configuration space to a measure-zero manifold and challenges conventional motion planners. We propose a fast bimanual motion planning pipeline that enforces this hard transformation constraint continuously along the entire path, using a leader-follower parameterization: the leader's configuration is treated as a free variable, while the follower's is determined via inverse kinematics to satisfy the constraint. We extensively evaluate the method in simulation across diverse environments, constraints and bimanual platforms, achieving 19.4x faster planning than prior work while guaranteeing continuous constraint satisfaction. Real-world experiments on a bimanual Kinova Gen3 setup, involving tray transport and elongated-object manipulation, validate direct transfer of planned trajectories to physical hardware.
Bimanual robotic assembly requires a deep synergy between global kinematic coordination and local physical stability, which is often hindered by complex coupling constraints and contact uncertainties. This work presents a hierarchical dual-manifold framework that decouples long-horizon generative planning from reactive contact adaptation. The planning layer employs a constraint-guided diffusion policy to ensure that generated trajectories conform to a geometric consistency manifold by incorporating task-level kinematic relationships as a differentiable guidance field. In synergy with this, a mode-aware residual policy operates on a physical interaction manifold, utilizing sparse kinesthetic corrections to provide fine-grained pose and wrench modulations for internal stress regulation. Both modules function within a receding-horizon architecture and are trained directly on real dual-arm hardware, bypassing the fidelity gaps inherent in simulated contact dynamics. We evaluate the system on a dual-UR5 platform across three representative tasks: rigid closed-chain insertion, semi-constrained cable routing, and decoupled stabilize-and-act insertion. Experimental results demonstrate that the framework consistently outperforms classical, learning-based, and ablated baselines across three coordination regimes, achieving 88–92% success rates while reducing interaction loads and improving contact robustness during bimanual assembly. Note to Practitioners—The motivation of this study is to address the challenge of jointly handling long-horizon geometric coordination and local contact adaptation in industrial bimanual assembly. Existing approaches often struggle to balance global motion consistency with rapid correction of contact-induced deviations under dual-arm coupling. We propose a hierarchical dual-manifold framework: a constraint-guided diffusion policy generates globally consistent dual-arm references, while a mode-aware residual policy compensates for local deviations during execution. Trained directly on real robot data, the framework reduces reliance on high-fidelity contact simulation and demonstrates robust performance across assembly tasks with different coordination modes. Its current limitation is that it mainly focuses on the assembly execution stage and does not yet include preceding operations such as grasping. Future work will extend the framework to more complex industrial geometries and multi-stage assembly scenarios.
Yu-Si Fan, Zhuang-Yu Liu, Chao Li et al.· IEEE Transactions on Automat...· 0 citations
Planning trajectories for robot manipulators under kinematic equality constraints restricts feasible motions to a measure-zero submanifold of the configuration space, requiring special algorithmic treatment. A promising strategy is parametrizing the set of feasible configurations using analytic inverse kinematics (IK). Bespoke analytic IK functions can be written to be differentiable, a necessary property for gradient-based trajectory optimization. But the vast majority of IK functions are computed by automated meta-solvers like IKFast, and are difficult to modify for differentiability. We present a new approach for computing gradients of analytic IK parameterizations: we leverage the inverse function theorem to recover the desired gradients from the ordinary forward kinematic Jacobian. Furthermore, we present a least-squares domain extension and an optimization-amenable description of the reachability constraint, which preserves gradient signal outside the reachable workspace. We demonstrate the efficacy of our approach through numerical experiments and downstream tasks, including a hardware demonstration of an RB-Y1 picking up a box and placing it on a table. Project website: https://cohnt.github.io/inverse-function-theorem-parameterization/
Thomas Cohn, Seiji Shaw, Harel Biggie et al.· 0 citations
Reconfigurable robots can change their contact geometry when a fixed body cannot negotiate an obstacle. A variable-geometry truss (VGT) distributes this shape change through a load-bearing structure, but coupling it to a mobile base creates a high-dimensional coordination problem. GeoTrussRover combines an electrically actuated VGT, a wheeled base, and contact-semantic morphology planning and control. We solve one source traversal and extract four contact-semantic primitives that describe coordination among 21 members. Physics-constrained projection adapts them to unseen step heights with the same contact topology. When every phase remains feasible, adaptation does not recompute the complete motion. If one phase violates the new physical constraints, only that phase is recomputed. A full-space QP then tracks the adapted motion and corrects member and wheel errors. For transfer from 0.10m to 0.075m, the method reduces objective-function evaluations by 63.7% relative to full recomputation. Contact-phase feasibility analysis covers step heights from 0.10 to 0.46m, or 1.08 to 4.97 wheel radii, with the upper value near the theoretical feasible boundary. The electric prototype traverses 2.11 wheel radii. The resulting low-dimensional representation stores task coordination in a hyper-redundant, load-bearing morphology and reuses it during locomotion.
Mu-Yuan Ma, Yi Zhang, Yang Yang et al.· 0 citations
This report adopts a two-piece MINCO parameterization, trading time for smoothness without altering the trajectory's spatial profile, and replaces score regression with a ranking loss, preventing small score errors from reordering the candidate set.
Mobile manipulators are increasingly tasked with transporting large, non-convex payloads through cluttered environments, yet existing planners either oversimplify the payload geometry or fail to handle the kinematic coupling between manipulator links, leading to lost feasible space or stalled optimization. This letter presents a real-time whole-body motion planning framework for mobile manipulators carrying arbitrarily shaped payloads. The front-end employs a chain-decomposed kernel-based collision check that preserves the true geometry of the robot and payload, with compact storage and fast bit-level queries. A mid-end preprocessing stage converts the front-end path into a continuous trajectory enforcing smoothness and feasibility, and executes it directly when collision-free to bypass the costly back-end. When refinement is required, the back-end performs trajectory optimization built on a Kinematically-Coupled SVSDF (KC-SVSDF), which propagates collision-avoidance gradients along the kinematic chain to produce coherent whole-body escape directions. Ablation studies, comparative benchmarks against state-of-the-art baselines, and real-world experiments on a differential-drive mobile manipulator demonstrate that the proposed framework reliably transports large, non-convex payloads through tight passages and cluttered environments.
Yisheng Li, Long-Ji Yin, Ting Zhang et al.· 0 citations
Robotic table tennis serves require high ball velocity and spin while respecting the robot's kinodynamic limits. Unlike rally strokes, a valid serve must also bounce on the server's side and clear the net, yielding a hybrid system with nonlinear flight and impact dynamics. We formulate spin-controlled serve generation as an event-time \ac{OCP} that optimizes the racket impact velocity and orientation together with the bounce, net-crossing, and landing times, enabling direct enforcement at phase boundaries of table-bounce and net-clearance constraints. The racket velocity and orientation are then converted into a complete kinodynamically feasible motion through a second \ac{OCP} enforcing joint-position, velocity, and torque limits. We evaluate the method numerically and on a KUKA Agilus robot. Compared with a fixed-step formulation with root localization, the proposed event-time formulation reduces the median solve time by a factor of 4.1 while maintaining comparable landing accuracy, spin accuracy, and serve validity. Real-robot experiments demonstrate controlled placement and topspin, backspin, and sidespin serves, with a mean landing error of $13.1 \pm 7.3$~cm and spin rates up to 30~rps. These results show that event-time optimal control efficiently generates physically valid, kinodynamically feasible serves while accounting for nonlinear aerodynamic and impact effects.
T. Gossard, Till Köpff, Andreas Ziegler· 0 citations
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