18 accepted papers
This paper addresses the challenge of safe and time-efficient crowd navigation for autonomous robots in dynamic environments. Existing methods struggle in scenarios with unpredictable or obstructive pedestrian behaviors. These limitations raise serious safety and efficiency concerns in real-world de
For long-horizon multi-task robotic manipulation, hierarchical approaches provide an effective way to combine high-level language-based task planning with low-level vision-language based sub-task execution. Then, we propose a framework that integrates a two-stage task planner with a multimodal low-l
Tactile texture is vital for robotic manipulation but challenging for camera vision-based observation. To address this, we propose TactileAloha, an integrated tactile-vision robotic system built upon Aloha, with a tactile sensor mounted on the gripper to capture fine-grained texture information and …
This letter proposes a bio-inspired, simple, and easy-to-implement walking controller, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Concerted Control</i>, which leverages a shared common signal to coordinate movements across multiple joints
Tactile texture is vital for robotic manipulation but challenging for camera vision-based observation. To address this, we propose TactileAloha, an integrated tactile-vision robotic system built upon Aloha, with a tactile sensor mounted on the gripper to capture fine-grained texture information and
Animal musculoskeletal systems are renowned for their ability to dynamically regulate stiffness and achieve energy-efficient motion. Being inspired by the biological control structure, this study presents a hybrid control framework that utilizes two-stage learning processes for body movement plannin
Humans have many redundancies in their bodies and can make effective use of them to adapt to changes in the environment while walking. They can also vary their walking speed in a wide range. Human-like walking in simulation or by robots can be achieved through imitation learning. However, the walkin
The latest robot navigation methods for dynamic environments assume that the states of obstacles, including their geometries and trajectories, are fully observable. While it’s easy to obtain these states accurately in simulations, it’s exceedingly challenging in the real world. Therefore, a viable a…
Navigation among pedestrians is a crucial capability of service robots; however, it is a challenge to manage time-varying environments stably. Recent deep reinforcement learning (DRL)-based approaches to crowd navigation have yielded numerous promising applications. However, they rely heavily on ini
Central Pattern generators (CPG) are a biologically inspired, decentralized control architecture that enables model-free, but yet adaptively stable and computational lightweight locomotion capabilities on complex robots. Nevertheless, no unified design guidelines for closed-loop CPG controllers are…
Insects exhibit adaptive walking behavior in an unstructured environment, despite having only an extremely small number of neurons (105 to 106). This suggests that not only the brain nervous system but also properties of the physical body, such as the morphological characteristics, play an essential…
Extracting motion information from videos is important for quantifying data from behavioral experiments to deepen the understanding of generation mechanisms of animal behavior. For insect walking, inter-leg coordination plays a crucial role, and the thorax-coxa (ThC) and femur-tibia (FTi) joint moti
Soft robotics is an emerging technology with excellent application prospects. However, due to the inherent compliance of the materials used to build soft robots, it is extremely complicated to control soft robots accurately. In this paper, we introduce a data-based control framework for solving the…
The robotic joint redundancy for executing a task and the optimal usage of robotic joints given the redundant degrees of freedom are crucial for the performance of a robot. It is therefore of interest to quantify the joint redundancy to better understand the robotic dexterity considering the dynamic…
Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, the
As human motor learning is hypothesized to use the motor synergy concept, we investigate if this concept could also be observed in deep reinforcement learning for robotics. From this point of view, we carried out a joint-space synergy analysis on multi-joint running agents in simulated environments
The development of algorithms for motion discrimination in home rehabilitation sessions poses numerous challenges. Recent studies have used the concept of synergies to discriminate a set of movements. However, the discrimination depends on the correlation of the reconstructed movement with the onlin…
Muscle synergies, which is the concept of modular activation of a set of muscles for producing complex motor behaviors, have been studied for a long time. Several definitions of muscle synergies have been proposed, and different algorithms have identified synergies in a large number of contexts. How