Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN

2025-07-02

AI Summary

Robot trajectory planning often relies on computationally intensive sampling-based methods or supervised imitation learning with poor generalization. This paper proposes a cognitive-inspired, self-supervised recurrent neural network framework that requires only forward and inverse kinematic models—no demonstration trajectories or large-scale labeled datasets. It performs end-to-end generation of kinematically feasible and smooth trajectories via goal-directed sequential self-supervised training. The key contribution is a closed-loop self-supervision mechanism: the forward model evaluates endpoint error to iteratively refine control sequences via the inverse model, eliminating conventional sampling and behavioral cloning paradigms. Evaluated on multi-DOF robotic arm tasks, the method achieves significantly higher planning efficiency and success rates compared to baselines. Moreover, it demonstrates strong potential for extension to complex, adaptive manipulation tasks requiring online adaptation and long-horizon reasoning.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence learning of trajectories, often requiring only a single or fixed number of passes through a neural architecture, thus ensuring a bounded computation time. Such fully supervised approaches, however, perform imitation learning; they do not learn based on whether the trajectories can successfully reach a goal, but try to reproduce observed trajectories. In our work, we build on this approach and propose a cognitively inspired self-supervised learning scheme based on a recurrent architecture for building a trajectory model. We evaluate the feasibility of the proposed method on a task of kinematic planning for a robotic arm. The results suggest that the model is able to learn to generate trajectories only using given paired forward and inverse kinematics models, and indicate that this novel method could facilitate planning for more complex manipulation tasks requiring adaptive solutions.

Problem

• Develop bio-inspired robotic trajectory planning method
• Replace computationally intensive sampling-based planners
• Enable adaptive solutions for complex manipulation tasks

Innovation

• Self-supervised RNN for trajectory planning
• Uses paired forward and inverse kinematics
• Cognitive-inspired adaptive robotic planning