# Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments

arXiv: https://arxiv.org/abs/2404.16138 · HTML: https://mlsanity.com/paper/2404.16138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 21 of 21)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Language Models Meet World Models: Embodied Experiences Enhance Language Models](https://mlsanity.com/paper/2305.10626/) (2023)
- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://mlsanity.com/paper/2003.06085/) (2020)
- [Imitating Task and Motion Planning with Visuomotor Transformers](https://mlsanity.com/paper/2305.16309/) (2023)
- [Object-Centric Task and Motion Planning in Dynamic Environments](https://mlsanity.com/paper/1911.04679/) (2019)
- [Human-in-the-Loop Task and Motion Planning for Imitation Learning](https://mlsanity.com/paper/2310.16014/) (2023)
- [Guided Imitation of Task and Motion Planning](https://mlsanity.com/paper/2112.03386/) (2021)
- [Representing Robot Task Plans as Robust Logical-Dynamical Systems](https://mlsanity.com/paper/1908.01896/) (2019)
- [Merging Position and Orientation Motion Primitives](https://mlsanity.com/paper/2003.11507/) (2020)
- [Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations](https://mlsanity.com/paper/2206.04632/) (2022)
- [Reactive Task and Motion Planning under Temporal Logic Specifications](https://mlsanity.com/paper/2103.14464/) (2021)
- [RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning](https://mlsanity.com/paper/2110.03420/) (2021)
- [Receding Horizon Task and Motion Planning in Changing Environments](https://mlsanity.com/paper/2009.03139/) (2020)
- [Learning to Sequence and Blend Robot Skills via Differentiable Optimization](https://mlsanity.com/paper/2206.00559/) (2022)
- [D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning](https://mlsanity.com/paper/2312.02731/) (2023)
- [Learning and Generalizing Variable Impedance Manipulation Skills from Human Demonstrations](https://mlsanity.com/paper/2104.01324/) (2021)
- [FC3: Feasibility-Based Control Chain Coordination](https://mlsanity.com/paper/2205.04362/) (2022)

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

- [FPC-VLA: A Vision-Language-Action Framework with a Supervisor for Failure Prediction and Correction](https://mlsanity.com/paper/2509.04018/) (2025)

## Abstract (arXiv)

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic Movement Primitives (Logic-DMP), which combines Task and Motion Planning (TAMP) with an optimal control formulation of DMP, allowing us to incorporate motion-level via-point specifications and to handle task-level variations or disturbances in dynamic environments. We conduct a comparative analysis of our proposed approach against several baselines, evaluating its generalization ability and reactivity across three long-horizon manipulation tasks. Our experiment demonstrates the fast generalization and reactivity of Logic-DMP for handling task-level variants and disturbances in long-horizon manipulation tasks.