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

arXiv: https://arxiv.org/abs/2507.02171 · HTML: https://mlsanity.com/paper/2507.02171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents](https://mlsanity.com/paper/2404.08825/) (2024)
- [myGym: Modular Toolkit for Visuomotor Robotic Tasks](https://mlsanity.com/paper/2012.11643/) (2020)
- [Computing Robust Inverse Kinematics Under Uncertainty](https://mlsanity.com/paper/1910.10852/) (2019)
- [Learning Low-Level Causal Relations using a Simulated Robotic Arm](https://mlsanity.com/paper/2410.07751/) (2024)
- [Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control](https://mlsanity.com/paper/2412.00541/) (2024)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance](https://mlsanity.com/paper/2607.20743/) (2026)

## Abstract (arXiv)

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.