# Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback

arXiv: https://arxiv.org/abs/2501.07507 · HTML: https://mlsanity.com/paper/2501.07507/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 93 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 30 of 37)

- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey](https://mlsanity.com/paper/2108.11451/) (2021)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Explainability in Human-Agent Systems](https://mlsanity.com/paper/1904.08123/) (2019)
- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Differentiable Inductive Logic Programming for Structured Examples](https://mlsanity.com/paper/2103.01719/) (2021)
- [Techniques for Symbol Grounding with SATNet](https://mlsanity.com/paper/2106.11072/) (2021)
- [Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees](https://mlsanity.com/paper/2308.09842/) (2023)
- [Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation](https://mlsanity.com/paper/1705.10422/) (2017)
- [Learning Reward Functions for Robotic Manipulation by Observing Humans](https://mlsanity.com/paper/2211.09019/) (2022)
- [TGRL: An Algorithm for Teacher Guided Reinforcement Learning](https://mlsanity.com/paper/2307.03186/) (2023)
- [Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach](https://mlsanity.com/paper/2304.08349/) (2023)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning](https://mlsanity.com/paper/2301.00452/) (2023)
- [Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation](https://mlsanity.com/paper/1707.02729/) (2017)
- [Autonomous task planning and situation awareness in robotic surgery](https://mlsanity.com/paper/2004.08911/) (2020)
- [Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation](https://mlsanity.com/paper/2303.03207/) (2023)
- [Conflict-driven Inductive Logic Programming](https://mlsanity.com/paper/2101.00058/) (2020)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Autonomous tissue retraction with a biomechanically informed logic based framework](https://mlsanity.com/paper/2109.02316/) (2021)
- [Learning Logic Specifications for Soft Policy Guidance in POMCP](https://mlsanity.com/paper/2303.09172/) (2023)
- [Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach](https://mlsanity.com/paper/2402.19265/) (2024)
- [Real-time analysis of cataract surgery videos using statistical models](https://mlsanity.com/paper/1610.05465/) (2016)
- [Weakly Supervised Recognition of Surgical Gestures](https://mlsanity.com/paper/1907.10993/) (2019)

## Built on by (most cited first, 2 of 2)

- [Modeling Arbitrarily Applicable Relational Responding with the Non-Axiomatic Reasoning System: A Machine Psychology Approach](https://mlsanity.com/paper/2503.00611/) (2025)
- [Explaining Reinforcement Learning Agents via Inductive Logic Programming](https://mlsanity.com/paper/2607.13655/) (2026)

## Abstract (arXiv)

The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities, possibly based on formal methods as logics for the definition of task specifications. However, prior knowledge is often unavailable in complex realistic scenarios. In this paper, we propose an offline algorithm based on inductive logic programming from noisy examples to extract task specifications (i.e., action preconditions, constraints and effects) directly from raw data of few heterogeneous (i.e., not repetitive) robotic executions. Our algorithm leverages on the output of any unsupervised action identification algorithm from video-kinematic recordings. Combining it with the definition of very basic, almost task-agnostic, commonsense concepts about the environment, which contribute to the interpretability of our methodology, we are able to learn logical axioms encoding preconditions of actions, as well as their effects in the event calculus paradigm. Since the quality of learned specifications depends mainly on the accuracy of the action identification algorithm, we also propose an online framework for incremental refinement of task knowledge from user feedback, guaranteeing safe execution. Results in a standard manipulation task and benchmark for user training in the safety-critical surgical robotic scenario, show the robustness, data- and time-efficiency of our methodology, with promising results towards the scalability in more complex domains.