Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing

Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and proprioception with binary, low-resolution tactile contact measurements from across the interior surface of an articulated robotic hand can mitigate this issue. The visuo-tactile object-pose-estimation problem is formulated probabilistically in a factor graph. The pose of the object is optimized to align with the three kinds of measurements using a robust cost function to reduce the influence of visual or tactile outlier readings. The advantages of the proposed approach are first demonstrated in simulation: a custom 15-DoF robot hand with one binary tactile sensor per link grasps 17 YCB objects while observed by an RGB-D camera. This low-resolution in-hand tactile sensing significantly improves object-pose estimates under high occlusion and also high visual noise. We also show these benefits through grasping tests with a preliminary real version of our tactile hand, obtaining reasonable visuo-tactile estimates of object pose at approximately 13.3 Hz on average.

A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions

A long-lasting goal of robotics research is to operate robots safely, while achieving high performance which often involves fast motions. Traditional motor-driven systems frequently struggle to balance these competing demands. Addressing this trade-off is crucial for advancing fields such as manufacturing and healthcare, where seamless collaboration between robots and humans is essential. We introduce a four degree-of-freedom (DoF) tendon-driven robot arm, powered by pneumatic artificial muscles (PAMs), to tackle this challenge. Our new design features low friction, passive compliance, and inherent impact resilience, enabling rapid, precise, high-force, and safe interactions during dynamic tasks. In addition to fostering safer human-robot collaboration, the inherent safety properties are particularly beneficial for reinforcement learning, where the robot's ability to explore dynamic motions without causing self-damage is crucial. We validate our robotic arm through various experiments, including long-term dynamic motions, impact resilience tests, and assessments of its ease of control. On a challenging dynamic table tennis task, we further demonstrate our robot's capabilities in rapid and precise movements. By showcasing our new design's potential, we aim to inspire further research on robotic systems that balance high performance and safety in diverse tasks. Our open-source hardware design, software, and a large dataset of diverse robot motions can be found at https://webdav.tuebingen.mpg.de/pamy2/.