5 papers
PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning
Lars Johannsmeier, Yashraj Narang
Real-world reinforcement learning (RL) systems still struggle with the demands of contact-rich industrial manipulation, including micrometer-level precision, success rates above 99…
SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly
Yijie Guo, Iretiayo Akinola, Lars Johannsmeier +3
Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of ro…
Point Bridge: 3D Representations for Cross Domain Policy Learning
Siddhant Haldar, Lars Johannsmeier, Lerrel Pinto +4
Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulatio…
Towards Task-Specific Modular Gripper Fingers: Automatic Production of Fingertip Mechanics
Johannes Ringwald, Samuel Schneider, Lingyun Chen +4
The number of sequential tasks a single gripper can perform is significantly limited by its design. In many cases, changing the gripper fingers is required to successfully conduct…
A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control
Lars Johannsmeier, Malkin Gerchow, Sami Haddadin
In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on ada…