5 papers
Design Paradigms Based on Spring Agonists for Underactuated Robot Hands: Concepts and Application
Tianjian Chen, Tianyi Zhang, Matei Ciocarlie
In this paper, we focus on a rarely used paradigm in the design of underactuated robot hands: the use of springs as agonists and tendons as antagonists. We formalize this approach…
Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning
Tianjian Chen, Zhanpeng He, Matei Ciocarlie
Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics. However, in most such cases, the hardware o…
Underactuation Design for Tendon-driven Hands via Optimization of Mechanically Realizable Manifolds in Posture and Torque Spaces
Tianjian Chen, Long Wang, Maximilan Haas-Heger +1
Grasp synergies represent a useful idea to reduce grasping complexity without compromising versatility. Synergies describe coordination patterns between joints, either in terms of…
Proprioception-Based Grasping for Unknown Objects Using a Series-Elastic-Actuated Gripper
Tianjian Chen, Matei Ciocarlie
Grasping unknown objects has been an active research topic for decades. Approaches range from using various sensors (e.g. vision, tactile) to gain information about the object, to…
Underactuated Hand Design Using Mechanically Realizable Manifolds
Tianjian Chen, Maximilian Haas-Heger, Matei Ciocarlie
Hand synergies, or joint coordination patterns, have become an effective tool for achieving versatile robotic grasping with simple hands or planning algorithms. Here we propose a m…