发表机构
National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究院（AIST））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对非夹持式机器人投掷，提出基于强化学习的方法，结合滑动与滚动接触模式，在仿真及真实UR5e机器人上实现高投掷成功率，可处理大型、沉重物体。
AI 中文摘要
机器人投掷可实现快速物体搬运，拓展机器人可达工作空间，超越传统的抓取-放置操作。夹持式（基于抓取）投掷适用于可抓取物品，而非夹持式（无抓取）投掷更适合大型、沉重和/或可变形物体。现有方法依赖基于模型的优化，采用简化的接触模型（如动态抓取）和低维轨迹参数化，这限制了解决方案质量和可达工作空间。我们提出一种强化学习方法，该方法额外利用滑动和滚动接触模式，无需解析接触模型或自定义参数化，直接优化关节空间轨迹。马尔可夫决策过程（MDP）被构建为一个动力学系统，其根据投掷目标、物体模型和初始配置演化机器人的关节状态。关节冲击轨迹以低控制率离线规划，并上采样为平滑的高率速度命令用于部署。为实现仿真到真实的迁移，我们通过最小冲击系统辨识最小化机器人动力学差距，并训练感知不确定性的策略以减轻物体建模误差，尤其是对动态摩擦的敏感性。在仿真中，该策略在数千种配置下达到99%的成功率，并能泛化到未见过的物体。敏感性分析显示其对质量不确定性具有鲁棒性，但对动态摩擦高度敏感，这与基于滑动的释放机制一致。在UR5e机器人（以接近其物理极限的5 m/s末端执行器速度运行）上零样本部署，我们的方法可将包括790克重物和20×20×28厘米大尺寸物体在内的各类物体投掷至最远350厘米距离或最高180厘米高度，在真实世界中达到97%的成功率。
英文摘要
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-based) throwing works well for graspable items, non-prehensile (grasp-free) throwing is better suited for large, heavy, and/or deformable objects. Existing approaches rely on model-based optimization with simplified contact models (e.g., dynamic grasping) and low-dimensional trajectory parameterizations, which limit solution quality and reachable workspace. We propose a reinforcement learning approach that additionally leverages sliding and rolling contact modes and directly optimizes joint-space trajectories without analytical contact models or custom parameterizations. The Markov Decision Process (MDP) is formulated as a dynamical system that evolves the robot's joint state conditioned on the throwing target, object model, and initial configuration. Joint-jerk trajectories are planned offline at a low control rate and upsampled into smooth, high-rate velocity commands for deployment. For sim-to-real transfer, we minimize the robot-dynamics gap through minimum-jerk system identification and train uncertainty-aware policies to mitigate object-modeling errors, particularly sensitivity to dynamic friction. In simulation, the policy achieves 99% success across thousands of configurations and generalizes to unseen objects. Sensitivity analysis shows robustness to mass uncertainty but high sensitivity to dynamic friction, consistent with the sliding-based release mechanism. Deployed zero-shot on a UR5e operating near its physical limits (5 m/s end-effector velocity), our method throws diverse objects including heavy (790 g) and large (20x20x28 cm) items to targets up to 350 cm distance or 180 cm elevation, achieving a 97% real-world success rate.
Comments8 pages, 9 figures, Accepted to IEEE IROS 2026
