What Bimanual Teleoperation and Learning from Demonstration Can Do (WBCD) Today (in 2025)
There have been lots of awesome works around the topic of bimanual teleoperation and learning from demonstration, employing various human sensing systems and robot embodiments. To name a few, ALOHA, Mobile ALOHA, DexCap, Open-TeleVision, HATO, GELLO, AirExo, UMI, etc. However, researchers design and evaluate on tasks that they come up with and perform teleoperation themselves. Also, there are lots of metrics that are critical for industry that are not evaluated in the academic research works, such as motion speed, system robustness, cost efficiencies, and difficulty to learn an effective policy.
Therefore, there has been a demand in the industry to set up a common set of tasks benchmark that is practically valuable and reflects various axes and levels of difficulty. The goal of this competition is to bring together researchers with industry and figure out the best fits between valuable tasks and technology solutions.
In this competition, participants are invited to use a wide range of ways: puppeteering, VR, exoskeleton, hand-held grippers, movement-tracking gloves, or algorithmic human hand sensing to perform challenging and practically valuable manipulation tasks and collect data meanwhile. We will evaluate the teams in terms of their task completion quality, data collection speed, and performance of policy learned using the collected data.”
