Nicholas Backshall — accepted AI-conference papers

2024

BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

Nikita Chernyadev, Nicholas Backshall, Xiao Ma, Yunfan Lu, Younggyo Seo, Stephen James

CoRL 2024poster

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide…

Cited by 10 Source ↗Scholar ↗

2024

Redundancy-Aware Action Spaces for Robot Learning

Pietro Mazzaglia, Nicholas Backshall, Xiao Ma, Stephen James

RA-L 2024

Joint space and task space control are the two dominant action modes for controlling robot arms within the robot learning literature. Actions in joint space provide precise control over the robot's pose, but tend to suffer from inefficient training; actions in task space boast data-efficient trainin

Cited by 6 Source ↗code ↗Scholar ↗