# Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Robotics

arXiv: https://arxiv.org/abs/2601.16985 · HTML: https://mlsanity.com/paper/2601.16985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 1 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 16 of 16)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping](https://mlsanity.com/paper/2011.02669/) (2020)
- [PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making](https://mlsanity.com/paper/1804.07779/) (2018)
- [Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning](https://mlsanity.com/paper/2010.13303/) (2020)
- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](https://mlsanity.com/paper/2012.13037/) (2020)
- [AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation](https://mlsanity.com/paper/2203.08994/) (2022)
- [RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments](https://mlsanity.com/paper/2206.12493/) (2022)
- [Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy](https://mlsanity.com/paper/1911.03849/) (2019)
- [Differential Assessment of Black-Box AI Agents](https://mlsanity.com/paper/2203.13236/) (2022)
- [Flexible and Efficient Long-Range Planning Through Curious Exploration](https://mlsanity.com/paper/2004.10876/) (2020)
- [A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies](https://mlsanity.com/paper/2204.03196/) (2022)

## Abstract (arXiv)

Adapting to unforeseen novelties in open-world environments remains a major challenge for autonomous systems. While hybrid planning and reinforcement learning (RL) approaches show promise, they often suffer from sample inefficiency, slow adaptation, and catastrophic forgetting. We present a neuro-symbolic framework integrating hierarchical abstractions, task and motion planning (TAMP), and reinforcement learning to enable rapid adaptation in robotics. Our architecture combines symbolic goal-oriented learning and world model-based exploration to facilitate rapid adaptation to environmental changes. Validated in robotic manipulation and autonomous driving, our approach achieves faster convergence, improved sample efficiency, and superior robustness over state-of-the-art hybrid methods, demonstrating its potential for real-world deployment.