Check out the Google Scholar page for a full and up-to-date publication list. ^* denotes equal contribution.

TL;DR: H2O enables real-time whole-body teleoperation of a full-sized humanoid to perform tasks like pick and place, walking, kicking, boxing, etc.

TL;DR: ABS enables fully onboard, agile (>3m/s), and collision-free locomotion for quadrupedal robots in cluttered environments.

TL;DR: We design a terrain-aware MPC framework that enables agile driving over uneven offroad geometries such as hills, banks, and ditches.

TL;DR: GYF is a safe falling and recovery framework that can actively tumble and recover to stable modes to reduce damage.

TL;DR: We quantify the convergence rate of sampling-based MPC, and design a practical and effective algorithm CoVO-MPC with optimal rate.

TL;DR: HMAC handles both manageable and latent disturbances with hierarchical iterative learning and smoothed streaming meta-learning.

TL;DR: SafeDPA jointly tackles the problems of policy adaptation and safe reinforcement learning, under unseen disturbances in the real world.

TL;DR: DMPO learns the inner-loop optimizer of sampling-based MPC directly via experience, outperforming MPC and end-to-end RL baselines.

TL;DR: We introduce a new aerial manipulation system that leverages tactile feedback for accurate contact force control and texture detection.

(Spotlight, 3.1%)

TL;DR: Not all model parameters are equally important. We develop an instance-optimal exploration algorithm for MBRL in nonlinear systems.

TL;DR: Inspired by robotics applications, we study algorithms for active representation learning with continuous task parametrization.

(Oral presentation, 6.6%)

TL;DR: DATT can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances.

TL;DR: CAJun is a hierarchical learning and control framework that enables legged robots to jump continuously with adaptive distances.

TL;DR: We combine adaptive nonlinear control and neural control for frequency restoration in power systems.

TL;DR: Neural-Fly uses adaptive control to online fine-tune a meta-pretrained DNN representation, enabling rapid adaptation in strong winds.

TL;DR: We propose a new online optimization setting with delay and nonlinear switching cost, and provide compeititve algorithms.

TL;DR: For online control with noisy predictions, we design an algorithm to optimally balance robustness and consistency (performance if no noise).

TL;DR: We study MPC's dynamic regret and competitive ratio in the presence of prediction inaccuracy and feedback delay.

(Spotlight, 2.9%)

TL;DR: We prove MPC's dynamic regret and competitive ratio exponentially improve as its prediction gets longer, in LTV systems.

TL;DR: We present an online multi-task learning approach for adaptive nonlinear control with non-asymptotic guarantees.

TL;DR: We develop a simple and efficient UQ method for 6-DoF pose estimation, and apply it in real-world grasping tasks.

TL;DR: Neural-Swarm is a learning-based controller and planner for close-proximity flight of heterogeneous multirotor swarms.

TL;DR: We derive an iterative algorithm to solve information-cost stochastic nonlinear optimal control problems for safe episodic learning.

TL;DR: We derive generalization bounds under domain shift and connect them with safety bounds in control, for end-to-end safe explorations.

TL;DR: We give the first non-asymptotic guarantee for MPC. MPC's dynamic regret exponentially decreases as its prediction gets longer.

TL;DR: We show competitive algorithms for a new class of online optimization problems, with applications in online competitive control.

TL;DR: We deploy Deep Sets to learn complex interactions between multirotors, for decentralized close-proximity control.

TL;DR: Spectrally normalized deep learning and nonlinear control enable provably stable agile drone landing.

TL;DR: We use inverse RL to learn reward models for human-like autonomous car following.