Contingency Games for Multi-Agent Interaction

Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the...

Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in...

Stable Motion Primitives via Imitation and Contrastive Learning

Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on...

A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system's underactuation, and the need of using a prominent feedforward control action to...

Robotic Skill Mutation in Robot-to-Robot Propagation During a Physically Collaborative Sawing Task

Skill propagation among robots without human involvement can be crucial in quickly spreading new physical skills to many robots. In this respect, it is a good alternative to pure reinforcement learning, which can be time-consuming, or learning from human demonstration, which requires human involvement. In the latter case, there may not be...

Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this...

Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement,...

Actuating Eigenmanifolds of Conservative Mechanical Systems Via Bounded or Impulsive Control Actions

Eigenmanifolds are two-dimensional submanifolds of the state space, which generalize linear eigenspaces to nonlinear mechanical systems. Initializing a robot on an Eigenmanifold (or driving it there by control) yields hyper-efficient and regular oscillatory behaviors, called modal oscillations. This letter investigates the possibility of...

Exciting efficient oscillations in nonlinear mechanical systems through Eigenmanifold stabilization

Nonlinear modes are a well investigated concept in dynamical systems theory, extending the celebrated modal analysis of linear mechanical systems to nonlinear ones. This letter moves a first step in the direction of combining control theory and nonlinear modal analysis towards the implementation of hyper-efficient oscillatory behaviors in...

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by...