Inductive Generalization for Robotic Manipulation

• URL: http://arxiv.org/abs/2606.20999v1
• Date: Fri, 19 Jun 2026 00:19:53 GMT
• Title: Inductive Generalization for Robotic Manipulation
• Abstract summary: We argue that to measure generalization capabilities we must instead test the inductive capacity of policies on progressively harder, out-of-distribution task variants.<n>We provide a reusable and formal evaluation protocol for measuring inductive generalization in any manipulation policy.
• Score: 27.207954193702008
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models learn structures that transfer across domains. However, in practice, visuomotor policies test performance by interpolation on known distributions using unstructured domain shifts (e.g. lighting, clutter, diverse objects). We argue that to measure generalization capabilities we must instead test the inductive capacity of policies on progressively harder, out-of-distribution task variants. We call this inductive generalization, drawing directly on how axis-based evaluation has revealed inherent generalization limitations in language models (e.g. sequence length, counting) arXiv:2502.00197 . We provide a reusable and formal evaluation protocol for measuring inductive generalization in any manipulation policy, and establish baselines showing that existing paradigms fail this test; e.g. SoTA Vision-Language-Action models and find that policies that appear to generalize to prior domain shifts (distractors, etc) fail inductive generalization tests. These results expose a class of learning challenges orthogonal to those addressed by data and model scaling in robot learning, yet are imperative to solve in order to realize general purpose robots.

Related papers

• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Benchmarking the Generality of Vision-Language-Action Models [0.9751538234290761]
  Generalist multimodal agents are expected to unify perception, language, and control.<n>We introduce MultiNet v1.0, a unified benchmark for measuring the cross domain generality of vision language models.
  arXiv  Detail & Related papers  (2025-12-12T06:31:52Z)
• Robust Finetuning of Vision-Language-Action Robot Policies via Parameter Merging [53.41119829581115]
  Generalist robot policies, trained on large and diverse datasets, have demonstrated the ability to generalize.<n>They still fall short on new tasks not covered in the training data.<n>We develop a method that preserves the generalization capabilities of the generalist policy during finetuning.
  arXiv  Detail & Related papers  (2025-12-09T08:02:11Z)
• AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation [59.671764778486995]
  Generalizing control policies to novel embodiments remains a fundamental challenge in enabling scalable and transferable learning in robotics.<n>We introduce a benchmark for learning cross-embodiment manipulation, focusing on two foundational tasks-reach and push-across a diverse range of morphologies.<n>We evaluate the ability of different RL policies to learn from multiple morphologies and to generalize to novel ones.
  arXiv  Detail & Related papers  (2025-05-21T00:21:38Z)
• A Taxonomy for Evaluating Generalist Robot Policies [29.437786401917702]
  evaluating and quantifying generalization is the Wild West of modern robotics.<n>We propose STAR-Gen, a taxonomy of generalization for robot manipulation structured around visual, semantic, and behavioral generalizations.<n>We evaluate a variety of state-of-the-art models on a benchmark to demonstrate the utility of our taxonomy in practice.
  arXiv  Detail & Related papers  (2025-03-03T07:03:00Z)
• Learning Model Successors [31.25792515137003]
  We argue that the unifying theme is induction, based on finite samples observed in training.<n>We propose a novel learning paradigm, Inductive Learning, which involves a central concept called model successors.
  arXiv  Detail & Related papers  (2025-01-31T22:27:09Z)
• Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding [85.63710017456792]
  FuSe is a novel approach that enables finetuning visuomotor generalist policies on heterogeneous sensor modalities.<n>We show that FuSe enables performing challenging tasks that require reasoning jointly over modalities such as vision, touch, and sound.<n>Experiments in the real world show that FuSeis able to increase success rates by over 20% compared to all considered baselines.
  arXiv  Detail & Related papers  (2025-01-08T18:57:33Z)
• Generalizable Imitation Learning Through Pre-Trained Representations [23.674399455529624]
  We introduce DVK, an imitation learning algorithm that leverages rich pre-trained Visual Transformer patch-level embeddings to obtain better generalization when learning through demonstrations.<n>Our learner sees the world by clustering appearance features into groups associated with semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types.
  arXiv  Detail & Related papers  (2023-11-15T20:15:51Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning [108.08083976908195]
  We show that policies learned by existing reinforcement learning algorithms can in fact be generalist. We show that a single generalist policy can perform in-hand manipulation of over 100 geometrically-diverse real-world objects. Interestingly, we find that multi-task learning with object point cloud representations not only generalizes better but even outperforms single-object specialist policies.
  arXiv  Detail & Related papers  (2021-11-04T17:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.