193 citations · 233 across the 8 of their papers we have counts for
8 papers
Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
Se Hwan Jeon, Steve Heim, Charles Khazoom +1
The main challenge in developing effective reinforcement learning (RL) pipelines is often the design and tuning the reward functions. Well-designed shaping reward can lead to signi…
Learning Fast and Precise Pixel-to-Torque Control
Steffen Bleher, Steve Heim, Sebastian Trimpe
In the field, robots often need to operate in unknown and unstructured environments, where accurate sensing and state estimation (SE) becomes a major challenge. Cameras have been u…
On exploration requirements for learning safety constraints
Pierre-François Massiani, Steve Heim, Sebastian Trimpe
Enforcing safety for dynamical systems is challenging, since it requires constraint satisfaction along trajectory predictions. Equivalent control constraints can be computed in the…
Safe Value Functions
Pierre-François Massiani, Steve Heim, Friedrich Solowjow +1
Safety constraints and optimality are important, but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to mai…
A Learnable Safety Measure
Steve Heim, Alexander von Rohr, Sebastian Trimpe +1
Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constr…
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Felix Grimminger, Avadesh Meduri, Majid Khadiv +10
We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor…
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace
Steve Heim, Alexander Spröwitz
Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very in…
Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
Steve Heim, Felix Ruppert, Alborz A. Sarvestani +1
Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, appl…