Recent advances in reinforcement learning (RL) have demonstrated impressive capabilities in complex decision-making tasks. This progress raises a natural question: how do these artificial systems compare to biological agents, which have been shaped by millions of years of evolution? To help answer this question, we undertake a comparative study of biological mice and RL agents in a predator-avoidance maze environment. Through this analysis, we identify a striking disparity: RL agents consistently demonstrate a lack of self-preservation instinct, readily risking ``death'' for marginal efficiency gains. These risk-taking strategies are in contrast to biological agents, which exhibit sophisticated risk-assessment and avoidance behaviors. Towards bridging this gap between the biological and artificial, we propose two novel mechanisms that encourage more naturalistic risk-avoidance behaviors in RL agents. Our approach leads to the emergence of naturalistic behaviors, including strategic environment assessment, cautious path planning, and predator avoidance patterns that closely mirror those observed in biological systems.

Human beings possess a remarkable skill for fine in-hand manipulation, utilizing both intrafinger interactions (in-finger) and finger–environment interactions across a wide range of daily tasks. These tasks range from skilled activities like screwing light bulbs, picking and sorting pills, and in-hand rotation, to more complex tasks such as opening plastic bags, cluttered bin picking, and counting cards. Despite its prevalence in human activities, replicating these fine motor skills in robotics remains a substantial challenge. This study tackles the challenge of fine in-hand manipulation by introducing the dexterous and compliant (DexCo) hand system. The DexCo hand mimics human dexterity, replicating the intricate interaction between the thumb, index, and middle fingers, with a contractable palm. The key to maneuverable fine in-hand manipulation lies in its innovative soft hydraulic actuation, which strikes a balance between control complexity, dexterity, compliance, and motion accuracy within a compact structure, enhancing the overall performance of the system. The model of soft hydraulic actuation, based on hydrostatic force analysis, reveals the compliance of hand joints, which is also further extended to a dedicated robot operating system (ROS) package for DexCo hand simulation, considering both motion and stiffness aspects. Dedicated velocity and position teleoperation controllers are designed for implementing real physical manipulation tasks. The benchmark results show that the fingertip achieves a maximum repeatable finger strength of 34.4 N, a grasp cycle time of less than 2.04 s, and a maximum repeatability accuracy of 0.03 mm. Experimental results demonstrate the DexCo hand successfully performs complex fine in-hand manipulation tasks, providing a promising solution for advancing robotic manipulation capabilities toward the human level.

This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and pushing are performed in the second stage. A small object bin picking system has been realized to exhibit the concept of two-stage grasping. Experiments have shown the effectiveness of the proposed framework. Unlike traditional bin picking methods focusing on vision-based grasping planning using classic frameworks, the challenges of picking cluttered small objects can be solved by the proposed new framework with simple vision detection and planning.

Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective, and rapidly deployable autonomous grasping approach is still limited by those target objects with fuzzy-depth information. Examples are transparent, specular, flat, and small objects whose depth is difficult to be accurately sensed. In this work, we present a solution to those fuzzy-depth objects. The framework of our approach includes two major components: one is a soft robotic hand and the other one is a Fuzzy-depth Soft Grasping (FSG) algorithm. The soft hand is replaceable for most existing soft hands/grippers with body compliance. FSG algorithm exploits both RGB and depth images to predict grasps while not trying to reconstruct the whole scene. Two grasping primitives are designed to further increase robustness. The proposed method outperforms reference baselines in unseen fuzzy-depth objects grasping experiments (84% success rate).