Autonomy Talks - Nadia Figueroa: From Motion to Interaction

Autonomy Talks - 05/11/24

Speaker: Prof. Nadia Figueroa, University of Pennsylvania

Title: From Motion to Interaction: A Dynamical Systems Approach for Human-Centric Robot Learning and Control

Abstract: For the last decades we have lived with the promise of one day being able to own a robot that can coexist, collaborate and cooperate with humans in our everyday lives. This has motivated a vast amount of research on robot control, motion planning, machine learning, perception and physical human-robot interaction (pHRI). However, we are yet to see robots fluidly collaborating with humans and other robots in the human-centric dynamic spaces we inhabit. This deployment bottleneck is due to traditionalist views of how robot tasks and behaviors should be specified and controlled. For collaborative robots to be truly adopted in such dynamic, ever-changing environments they must be adaptive, compliant, reactive, safe and easy to teach or program. Combining these objectives is challenging as providing a single optimal solution can be intractable and even infeasible due to problem complexity, time-critical, safety-critical requirements and contradicting goals. In this talk, I will show that with a Dynamical Systems (DS) approach for motion planning and pHRI we can achieve reactive, provably safe and stable robot behaviors while efficiently teaching the robot complex tasks from a single (or handful of) demonstration. Such an approach can be extended to offer task-level reactivity, transferability and can be used to incrementally learn from new data and failures in a matter of seconds and even during physical interactions, just as humans do. Furthermore, I will show that such DS perspective to robot motion planning naturally allows for compliant and passive robot behaviors that inherently ensure human safety. While reactivity and compliance are favorable from the human perspective, it is often difficult to enforce any type safety-critical constraints for the robot with classical reactive and impedance control techniques -- leading roboticists to favor optimization-based techniques such as MPC. Hence, I will finalize the talk showing some recent work where we offer the best of both worlds, real-time reactivity and compliance while ensuring safety-critical constraints allowing the robot to be passive only when feasible and performing constraint-aware physical interaction tasks with humans such as dynamic co-manipulation of large and heavy objects.