Agile Robot Control and Design (ARCAD) Lab

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity

We introduce SurGE, a co-design framework that guides an evolutionary search with an gradient from a differentiable model and a design aware control policy to jointly optimize the spring hardware and the controller, reducing the design objective by 37.65% on hardware while converging about 6 times more consistently than standard evolutionary search.

IROS 2026 | Website | arXiv | Video | Code

Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot

We introduce a hierarchical control framework that enables wheeled bipedal robots to slide objects with their legs, combining robot-object motion planning, nonlinear model predictive control based on a a novel reduced-order model, and whole-body control. Hardware experiments demonstrate under-desk object retrieval and dynamic scooting.

RAL 2026 | Website | arXiv | Video 

Kinodynamic Model Predictive Control for Energy Efficient Locomotion of Legged Robots with Parallel Elasticity

We introduce a kinodynamic model predictive control (MPC) framework that exploits unidirectional parallel springs (UPS) to improve the energy efficiency of dynamic legged robots. Preliminary hardware experiments show a 14.8% reduction in energy consumption.

ICRA 2025  | arXiv | Video

Contact Sensing via Joint Torque Sensors and a Force/Torque Sensor for Legged Robots

We propose a generalized momentum-based observer framework for detecting and localizing contact with data from (a) a hip-mounted force-torque (FT) sensor and (b) distributed low-cost strain-gauge-based joint torque sensors.

CASE 2025