A Biologically Inspired Design Principle for Building Robust Robotic Systems

TL;DR

This work proposes a biologically inspired design principle—active interconnections among perception, control, and planning—to build robust robotic systems. By applying this principle to a challenging lockbox manipulation task, the authors show that increasing the degree of active interconnections improves planning efficiency and real-world robustness, even under varying lockbox configurations, scales, and end-effector morphologies. The approach combines perception-driven grasp pose estimation, wrench-gated manipulation, online environment feature extraction, and attention-guided planning, enabling reuse of manipulation models and adaptive exploration without extensive retraining. The findings suggest that architectural patterns emphasizing active interconnections can substantially enhance robustness, and invite further interdisciplinary research to uncover additional biology-inspired principles for resilient autonomous manipulation.

Abstract

Robustness, the ability of a system to maintain performance under significant and unanticipated environmental changes, is a critical property for robotic systems. While biological systems naturally exhibit robustness, there is no comprehensive understanding of how to achieve similar robustness in robotic systems. In this work, we draw inspirations from biological systems and propose a design principle that advocates active interconnections among system components to enhance robustness to environmental variations. We evaluate this design principle in a challenging long-horizon manipulation task: solving lockboxes. Our extensive simulated and real-world experiments demonstrate that we could enhance robustness against environmental changes by establishing active interconnections among system components without substantial changes in individual components. Our findings suggest that a systematic investigation of design principles in system building is necessary. It also advocates for interdisciplinary collaborations to explore and evaluate additional principles of biological robustness to advance the development of intelligent and adaptable robotic systems.

Figures (11)

• Figure 1: Our robotic system solving a mechanical puzzle called lockbox (left). Our novel lockbox serves as a challenging testbed for evaluating the robustness of our system. Our hardware platform consists of a Franka Emika Panda arm equipped with an RBO Hand 3 end-effector puhlmann2022rbo, an RGB-D camera, and a Force/Torque sensor. The three fundamental components of our system are depicted as circles: Perception, Control, and Planning (right). The four intersection regions of these components depict new behaviors emerging from actively interconnecting these components: a design principle inspired from biological agents. These active interconnections lead to an increased robustness in solving the lockbox as will be discussed in this paper.
• Figure 2: Joint interdependencies as Directed Acyclic Graphs (DAGs): The One-to-One example (Figure \ref{['fig:one2one']}) depicts joint $\mathbf{B}$ depending on joint $\mathbf{A}$ being in state 1. The Many-to-One example (Figure \ref{['fig:many2one']}) shows joint $\mathbf{D}$ requiring joints $\mathbf{A}$, $\mathbf{B}$, and $\mathbf{C}$ in the respective states 0, 1, and 1 to unlock. Joint $\mathbf{A}$, in Figure \ref{['fig:bi_stable']} is a bistable-locking joint, which locks joints $\mathbf{B}$ and $\mathbf{C}$ in two different states.
• Figure 3: Our physical Lockbox overlayed by the joint-interdependencies graph. The blue marked nodes depict the additional fictive joints used in the simulation. The directed acyclic graph visualizes the interdependency structure.
• Figure 4: Illustration of our robotic system with different active interconnections among components. The fundamental components—perception, control, and planning—are depicted as circles, with overlapping regions showcasing new behaviors resulting from the active interconnections.
• Figure 5: Autonomous manipulation of joint D by simultaneous estimation and following of the admissible motion direction. The force measurements observed in the end-effector frame are illustrated below, depicting three distinct phases of manipulation. In the first phase (highlighted in yellow), the robot successfully follows the admissible motion direction of joint D, aligned with the y-axis of the end-effector's frame. As the robot arrives at the motion limits of joint D, it also reaches the predefined force limit of 10N in the y-axis direction (shown in orange). Subsequently, the robot begins exploring other directions (indicated in purple), leading to different force measurements. However, movement in these directions is undesirable, as they result from deformations of the soft end-effector rather than from joint D, potentially causing issues like losing contact with the handle. By establishing an active interconnection, the robot efficiently regulates force within the predefined force limit (10N) during this exploration, ensuring successful manipulation with the soft end-effector.