Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Ablett, Trevor, Limoyo, Oliver, Sigal, Adam, Jilani, Affan, Kelly, Jonathan, Siddiqi, Kaleem, Hogan, Francois, Dudek, Gregory
–arXiv.org Artificial Intelligence
Abstract--Kinesthetic Teaching is a popular approach to collecting expert robotic demonstrations of contact-rich tasks for imitation learning (IL), but it typically only measures motion, ignoring the force placed on the environment by the robot. Furthermore, contact-rich tasks require accurate sensing of both reaching and touching, which can be difficult to provide with conventional sensing modalities. We address these challenges with a See-Through-your-Skin (STS) visuotactile sensor, using the sensor both (i) as a measurement tool to improve kinesthetic teaching, and (ii) as a policy input in contact-rich door manipulation tasks. An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor. First, we propose tactile force matching, a methodology that enables a robot to match forces read during kinesthetic teaching using tactile signals. Second, we develop a policy that controls STS mode switching, allowing a policy to learn the appropriate moment to switch an STS from its visual to its tactile mode. Finally, we study multiple observation configurations to compare and contrast the value of visual and tactile data from an STS with visual data Figure 1: Our STS sensor before and during contact with a cabinet knob from a wrist-mounted eye-in-hand camera. In visual mode, the camera sees through episodes from real-world manipulation experiments, we find that the gel and allows finding and reaching the knob, while tactile mode the inclusion of force matching raises average policy success rates provides contact-based feedback, via gel deformation and resultant by 62.5%, STS mode switching by 30.3%, and STS data as a dot displacement, upon initial contact and during opening. This dot policy input by 42.5%. Our results highlight the utility of seethrough displacement can also be used to measure a signal linearly related to tactile sensing for IL, both for data collection to allow force. Red circles highlight knob in sensor view.
arXiv.org Artificial Intelligence
Dec-22-2023
- Country:
- Europe (1.00)
- North America
- Genre:
- Research Report > New Finding (1.00)
- Industry:
- Education (0.46)
- Health & Medicine > Therapeutic Area (0.46)
- Technology:
- Information Technology > Artificial Intelligence > Robots > Manipulation (1.00)