The MOTIF Hand: A Robotic Hand for Multimodal Observations with Thermal, Inertial, and Force Sensors

Advancing dexterous manipulation with multi-fingered robotic hands requires rich sensory capabilities, while existing designs lack onboard thermal and torque sensing. In this work, we propose the MOTIF hand, a novel multimodal and versatile robotic hand that extends the LEAP hand by integrating: (i) dense tactile information across the fingers, (ii) a depth sensor, (iii) a thermal camera, (iv), IMU sensors, and (v) a visual sensor. The MOTIF hand is designed to be relatively low-cost (under 4000 USD) and easily reproducible. We validate our hand design through experiments that leverage its multimodal sensing for two representative tasks. First, we integrate thermal sensing into 3D reconstruction to guide temperature-aware, safe grasping. Second, we show how our hand can distinguish objects with identical appearance but different masses - a capability beyond methods that use vision only.

Learning to Singulate Objects in Packed Environments using a Dexterous Hand

Robotic object singulation, where a robot must isolate, grasp, and retrieve a target object in a cluttered environment, is a fundamental challenge in robotic manipulation. This task is difficult due to occlusions and how other objects act as obstacles for manipulation. A robot must also reason about the effect of object-object interactions as it tries to singulate the target. Prior work has explored object singulation in scenarios where there is enough free space to perform relatively long pushes to separate objects, in contrast to when space is tight and objects have little separation from each other. In this paper, we propose the Singulating Objects in Packed Environments (SOPE) framework. We propose a novel method that involves a displacement-based state representation and a multi-phase reinforcement learning procedure that enables singulation using the 16-DOF Allegro Hand. We demonstrate extensive experiments in Isaac Gym simulation, showing the ability of our system to singulate a target object in clutter. We directly transfer the policy trained in simulation to the real world. Over 250 physical robot manipulation trials, our method obtains success rates of 79.2%, outperforming alternative learning and non-learning methods.