Modeling, Planning, and Learning for Soft Robots in Human-Centric, Contact-Rich Environments

Abstract

Robots are increasingly moving from constrained, structured settings (e.g., assembly lines and warehouses) to less controlled environments (e.g., construction sites, hospitals, or our homes), where they can enhance human capabilities and assist in patient care or activities of daily living. In these unstructured settings, robots are required to share their workspace with — and understand — human collaborators, operate reliably under uncertainty, and impart precisely controlled forces on fragile objects without damaging them. Soft robots, in contrast to their rigid counterparts, can gently interact with the world despite failures or planning inaccuracies via passive compliance in their materials and/or structures. However, their inherent compliance, in combination with the fact that they commonly undergo non-linear and high-dimensional deformations, can make it hard to design and control them, so far hindering their widespread use in human-robot collaborative tasks. From using machine learning to infer human gestures from garment-integrated sensors, to the development of new computational tools for manufacturing, planning, and reinforcement learning for soft robots in contact-rich environments, this thesis explores how rigorous modeling and computational tools can advance the capabilities of soft robots to enable their effective and uninterrupted cooperation with humans.

We first present the development of a sensorized sleeve and demonstrate the ability to detect hand gestures without encumbering the operator’s hand, highlighting the sleeve’s utility as a seamless human-robot interface. After this foray into sensing, the remainder of this thesis discusses various approaches to improve the capabilities of soft robot actuators. We present two computational tools to facilitate the design of soft robots and their controllers at two different levels of abstraction: one suitable to accelerate and automate design iterations under consideration of detailed material deformation and manufacturing requirements; and one suitable for the exploration of system-level design choices in simulation. We demonstrate the utility of both of these tools through a number of design studies on soft robot hands and continuum arms. Driven by the need to generate contact-rich trajectories for these systems as they complete in-hand manipulation tasks or navigate clutter, we then introduce a novel framework for path planning that explicitly accounts for the effect of contact forces along the full length of tentacle-like soft manipulators. Finally, we present a benchmark and training paradigm that facilitate the development of high-level controllers for soft robots using reinforcement learning, and show how these tools enable soft robots to learn a diverse set of skills ranging from locomotion to in-hand manipulation. Altogether, this thesis presents wearable sensors that enable soft robots to understand an operator’s intent, and extends the capabilities of soft robots to reason about and reliably execute a complex series of actions in order to assist the operator in meeting their goals.