When a robot observes another agent unexpectedly modifying their behavior, inferring the most likely cause is a valuable tool for maintaining safety and reacting appropriately. In this work, we present a novel method for inferring constraints that works on continuous, possibly sub-optimal demonstrations. We first learn a representation of the continuous-state maximum entropy trajectory distribution using deep reinforcement learning. We then use Monte Carlo sampling from this distribution to generate expected constraint violation probabilities and perform constraint inference. When the demonstrator's dynamics and objective function are known in advance, this process can be performed offline, allowing for real-time constraint inference at the moment demonstrations are observed. We evaluate our approach on two continuous dynamical systems: a 2-dimensional inverted pendulum model, and a 4-dimensional unicycle model that was successfully used for fast constraint inference on a 1/10 scale car remote-controlled by a human.

Motion optimization for legible robot intent has largely ignored the robot’s dynamics, citing burdensome complexity that prevents online deployment. Even where the original task (to be communicated) could be solved on the dynamical system, the legibility problem (to communicate that task’s intent) could not. This work simplifies the legibility objective to have equivalent computational complexity as the original objective to be communicated. This enables any optimal control algorithm that can solve the original task to also solve the legible version of that task.Along the way, we expand the definition of "intent" to include any parameter of the optimal control problem, thereby opening the door to extend communications beyond merely desired end-points to running preferences or even, in the future, hard capabilities or safety constraints. We demonstrate how this method can replicate the properties introduced in previous communicative motion state-of-the-art (like legibility, exaggeration, and anticipation) as well as apply to non-holonomic dynamical systems.

The minimalist robot designs typically employed in swarms and teams can fall and get trapped when traversing irregular terrain. To protect against this contingency the design could add a specialized escape actuator, but each actuator drives up cost multiplicatively for the whole team. Instead, the emergency actuator can be found for free in the form of another teammate. Teammate pushing can be efficiently directed by careful shaping of the robot's exterior hull. This approach is illustrated by designing a shell for VelociRoACH robots that enables them to roll pronated comrades back onto their feet. The designed maneuver can be performed in open-loop with 87% success and an average time of 0.7 seconds.

When a human supervisor collaborates with a team of robots, the human's attention is divided, and cognitive resources are at a premium. We aim to optimize the distribution of these resources and the flow of attention. To this end, we propose the model of an idealized supervisor to describe human behavior. Such a supervisor employs a potentially inaccurate internal model of the the robots' dynamics to judge safety. We represent these safety judgements by constructing a safe set from this internal model using reachability theory. When a robot leaves this safe set, the idealized supervisor will intervene to assist, regardless of whether or not the robot remains objectively safe. False positives, where a human supervisor incorrectly judges a robot to be in danger, needlessly consume supervisor attention. In this work, we propose a method that decreases false positives by learning the supervisor's safe set and using that information to govern robot behavior. We prove that robots behaving according to our approach will reduce the occurrence of false positives for our idealized supervisor model. Furthermore, we empirically validate our approach with a user study that demonstrates a significant (p = 0.0328) reduction in false positives for our method compared to a baseline safety controller.