upper/lower-arm coordinated motion planning and synchronization

This study addresses the lack of a unified and portable real-time low-level motion planning interface for heterogeneous collaborative robotic arms. To bridge this gap, the authors propose a lightweight and flexible real-time end-effector trajectory control interface built upon the WinGs Operating Studio middleware. The approach integrates n-th-order polynomial interpolation with a quadratic programming (QP) solver to generate smooth, continuously differentiable trajectories that enable precise control over position, velocity, and acceleration. For the first time, real-time low-level motion planning across multiple brands of collaborative arms is achieved under a single interface, featuring on-the-fly replanning capability and cross-platform compatibility. Experimental validation through offline drawing, dynamic grasping, and cross-arm teleoperation demonstrates significant improvements in system generality, deployment efficiency, and usability.

This paper addresses the challenge of human–robot collaboration under communication delays in human trajectory information and complete uncertainty in the robot’s kinematic and dynamic models. Method: We propose a task-space adaptive synchronization controller that, for the first time, integrates Barrier Lyapunov Functions (BLFs) with Lyapunov–Krasovskii functionals to construct a delay-compensation mechanism. Unknown model parameters are jointly estimated online via two complementary adaptive laws: one based on iterative composite learning (ICL) and the other on gradient descent—ensuring real-time synchronization while respecting safety constraints. Contribution/Results: The closed-loop error system is proven semi-globally uniformly ultimately bounded (SGUUB). Simulation results demonstrate high-precision tracking of delayed human trajectories under typical communication delays, while rigorously satisfying state constraints and collaborative safety requirements.

Addressing challenges in single- and multi-arm cooperative manipulation—including strong force-motion coupling, seamless switching between free motion and contact interaction over long time horizons, and lack of joint object-environment constraint modeling for inter-arm synchronization—this paper proposes a dynamically switchable three-modal control framework: pure planning, pure force control, and hybrid coordination. We introduce, for the first time, a task-driven dynamic modality allocation mechanism and systematically support joint object-environment constraint modeling in multi-arm settings. The method integrates impedance/admittance-based force control, nonlinear optimization-based motion planning (SQP/OC), real-time mode scheduling, and multibody dynamics modeling. Evaluated on long-horizon tasks—including single-arm assembly, dual-arm flipping, and tri-arm transport—the approach achieves a 42% reduction in contact force error, a 35% improvement in trajectory tracking accuracy, and a 98.7% task success rate.

Addressing the challenges of coordination and poor real-time performance in dual-arm collaborative robots intercepting high-speed dynamic objects under closed-chain constraints, this paper proposes an adaptive terminal nonlinear model predictive control (NMPC) framework. The method integrates cost shaping with real-time joint-space motion planning to tightly couple dynamic trajectory generation and closed-loop control—thereby simultaneously ensuring motion agility, significantly reducing control energy consumption, and enhancing robustness. Experimental results demonstrate an average planning cycle of only 19 ms—less than half the system’s sampling period—enabling millisecond-level response and high-precision dynamic interception. The proposed approach is validated to achieve superior computational efficiency, motion quality, and constraint satisfaction.

Long-horizon collaborative tasks for dual robotic arms face challenges including complex spatiotemporal dependencies among subtasks, difficulty in dynamic action allocation, and limited expressiveness of linear programming formulations. This paper proposes the first LLM-driven DAG-structured task decomposition framework, which automatically parses high-level instructions into directed acyclic graphs (DAGs) encoding dependency constraints, and integrates environment perception to enable real-time, dynamic action allocation and parallel adaptive execution across both arms. The method breaks away from predefined operational paradigms, supporting end-to-end, interpretable, and generalizable collaborative planning. Evaluated on the Dual-Arm Kitchen benchmark, it achieves a 52.8% efficiency gain over single-arm systems, improves success rate by 48% and reduces LLM query count by 84.1% compared to conventional dual-arm planners, significantly enhancing robustness and scalability in complex scenarios.

This work addresses the challenges of deploying learned multi-hand manipulation policies on multi-arm robotic systems, which involve trajectory assignment, kinematic constraints, and collision avoidance. The authors propose a unified conflict-based search framework that jointly models the discrete assignment of trajectories to arms and continuous motion planning within the nullspace of redundant-arm Jacobians—an integration not previously achieved. By doing so, the method ensures precise end-effector tracking of policy-specified trajectories while respecting kinematic limits and avoiding collisions, thereby overcoming the safety and feasibility limitations inherent in conventional single-arm inverse kinematics extensions. Experimental results demonstrate that the approach enables efficient and reliable embodiment of multi-hand policies on real multi-arm platforms, exhibiting both theoretical soundness and practical effectiveness.

该论文针对多机械臂系统的协调运动规划问题，提出了一种基于迭代线性二次型游戏的方法，通过引入可微分的碰撞惩罚项实现高效、安全的轨迹生成。

This work addresses the “execution gap” between high-level semantic tasks and executable robot motions by introducing Motion Statecharts—a symbolic, executable motion representation that supports concurrency and hierarchical nesting. Coupled with a unified differentiable kinematic world model, this framework enables end-to-end mapping from semantic task specifications to low-level motion control. Smooth and dynamically feasible trajectories are generated through a linear model predictive control (lMPC)-driven task-function approach incorporating snap (jerk derivative) constraints. The proposed system has been successfully deployed across eight heterogeneous robotic platforms, demonstrating strong cross-platform generalization and real-world efficacy. The accompanying software framework, Giskard, has been publicly released.

This work addresses the limitations of classical trajectory planning methods, which prioritize kinematic smoothness while neglecting dynamics and actuator control effort, often resulting in large tracking errors and high energy consumption. To overcome these issues, the authors propose a control-aware optimal trajectory planning framework that explicitly integrates the nonlinear dynamics of robotic manipulators and actuator effort over a finite time horizon. A midpoint linearization strategy is introduced to enhance the accuracy of dynamic approximations during large-range motions. By establishing a unified nonlinear closed-loop simulation environment, the study enables, for the first time, an isolated evaluation of trajectory generation methods under identical conditions. Experimental results on a simplified UR5 model demonstrate that the proposed approach significantly reduces tracking error, corrective torque, and overall closed-loop execution cost, achieving substantially lower energy consumption and total operational expense compared to conventional planners such as cubic, quintic, and trapezoidal profiles.

This work addresses the challenge of real-time motion planning for complex robotic systems under geometric constraints, which is often hindered by high computational costs. For the first time, SIMD parallelization is introduced into manifold-constrained motion planning by reformulating projection operations into a parallelizable structure that leverages CPU SIMD instruction sets to efficiently accelerate constraint satisfaction. The proposed method dramatically improves computational efficiency, enabling real-time whole-body quasi-static motion planning on a physical humanoid robot. Experimental results demonstrate speedups of 100 to 1,000 times compared to existing approaches, while maintaining accuracy and feasibility under stringent geometric constraints.