BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide human-collected demonstrations for each task, reflecting the diverse modalities found in real-world robot trajectories. BiGym supports a variety of observations, including proprioceptive data and visual inputs such as RGB, and depth from 3 camera views. To validate the usability of BiGym, we thoroughly benchmark the state-of-the-art imitation learning algorithms and demo-driven reinforcement learning algorithms within the environment and discuss the future opportunities.

Development of MirrorShape: High Fidelity Large-Scale Shape Rendering Framework for Virtual Reality

Today there is a high variety of haptic devices capable of providing tactile feedback. Although most of existing designs are aimed at realistic simulation of the surface properties, their capabilities are limited in attempts of displaying shape and position of virtual objects. This paper suggests a new concept of distributed haptic display for realistic interaction with virtual object of complex shape by a collaborative robot with shape display end-effector. MirrorShape renders the 3D object in virtual reality (VR) system by contacting the user hands with the robot end-effector at the calculated point in real-time. Our proposed system makes it possible to synchronously merge the position of contact point in VR and end-effector in real world. This feature provides presentation of different shapes, and at the same time expands the working area comparing to desktop solutions. The preliminary user study revealed that MirrorShape was effective at reducing positional error in VR interactions. Potentially this approach can be used in the virtual systems for rendering versatile VR objects with wide range of sizes with high fidelity large-scaleshape experience.