Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical physical execution remains a significant challenge. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in practical applications. … [Read more...] about I-CTRL: Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning
Robot sensing systems
Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion
Reinforcement learning (RL) offers a promising solution for controlling humanoid robots, particularly for bipedal locomotion, by learning adaptive and flexible control strategies. However, direct RL application is hindered by time-consuming trial-and-error processes, necessitating training in simulation before real-world transfer. This introduces a reality gap that degrades … [Read more...] about Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion
Digital Robot Judge: Building a Task-Centric Performance Database of Real-World Manipulation With Electronic Task Boards
Robotics aims to develop manipulation skills approaching human performance. However, skill complexity is often over- or underestimated based on individual experience, and the real-world performance gap is difficult or expensive to measure through in-person competitions. To bridge this gap, we propose a compact, internet-connected, electronic task board to measure manipulation … [Read more...] about Digital Robot Judge: Building a Task-Centric Performance Database of Real-World Manipulation With Electronic Task Boards
Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes … [Read more...] about Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
Variable Stiffness, Sensing, and Healing in FESTO’s FinRay Gripper: An Industry-Driven Design
Soft grippers' rising popularity in industries is due to their impressive adaptability. Yet, this adaptability requires flexibility which often sacrifices grip firmness and complicates sensor integration. This paper introduces two additional innovations, variable stiffness and pneumatic sensing, into a FinRay adaptive gripper. The approach and design for incorporating these … [Read more...] about Variable Stiffness, Sensing, and Healing in FESTO’s FinRay Gripper: An Industry-Driven Design
