103 citations · 484 across the 48 of their papers we have counts for
58 papers
Training Controllable World Models via Action Influence Maximization
Samuel M. Bateman, Tenny Yin, Chongyi Zheng +5
Action-conditioned video generative models trained on successful, goal-directed demonstrations tend to ignore the commanded action, rendering a plausible expert-like future regardl…
WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions
Haoyi Duan, Liliang Chen, Nan Wang +13
We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions. Given the same initial observa…
Training Generalist Navigation Policies via Action Self-Distillation
Bowen Feng, Keqin Wang, Yulong Yang +4
Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning typically requires expensive ma…
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Lihan Zha, Shresth Grover, Tenny Yin +7
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for c…
EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics
Haoxiang You, Zeyu Shen, Yilang Liu +16
We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we d…
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Anqi Li, Yuxin Chen, Zhaobo Li +4
We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid tr…
Emergent Compositional Skills in Mixture-of-Experts VLAs
Shlok Shah, Rhiaan Jhaveri, Tharun Kumar Tiruppali Kalidoss +3
We consider the problem of learning compositional robot policies end-to-end from expert demonstrations, without any pre-specified notion of task decomposition or hierarchy. We ask…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Addressing the Orchestration Gap in Generalist Robots via Physical Agency
Liane Galanti, Dhruv Shah, Tri Dao
General-purpose robots need to reason about their actions, combining perception, world knowledge, planning, success detection, recovery, and low-level control. Today's state-of-the…
RoboVista: Evaluating Vision Language Models for Diverse Robot Applications
Shuangyu Xie, Kaiyuan Chen, Ziyang Chen +8
Diverse applications for robotics, such as industry and agriculture, require robots to operate across various embodiments, changing visual conditions, and complex planning. Vision-…
RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory
Yixun Hu, Zhicheng Zheng, Lihan Zha +5
Long-term robot deployment requires a compact and scalable memory that preserves fine-grained visual semantics, grounds observations in space and time, and enables efficient storag…
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
Mingtong Zhang, Dhruv Shah
Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we pr…
Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
Dhruv Shah, Jorge Cortés
The accuracy of Koopman operator approximations over finite-dimensional spaces relies critically on their invariance properties. These can be rigorously quantified via the principa…
What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents
Jiaheng Hu, Mohit Shridhar, Caden Lu +4
Hierarchical vision-language-action (Hi-VLA) systems have emerged as a promising paradigm for complex robot manipulation, by using high-level VLM planners to decompose tasks into l…
WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform
Yu Shang, Yinzhou Tang, Yiding Ma +22
World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about environmental dynamics. However, ex…
BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames
Max Sobol Mark, Jacky Liang, Maria Attarian +4
Many robot tasks require attending to the history of past observations. For example, finding an item in a room requires remembering which places have already been searched. However…
AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge
Noriaki Hirose, Catherine Glossop, Dhruv Shah +1
Robotic foundation models achieve strong generalization by leveraging internet-scale vision-language representations, but their massive computational cost creates a fundamental bot…
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Kanav Arora +11
A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169
General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…
OmniVLA: An Omni-Modal Vision-Language-Action Model for Robot Navigation
Noriaki Hirose, Catherine Glossop, Dhruv Shah +1
Humans can flexibly interpret and compose different goal specifications, such as language instructions, spatial coordinates, or visual references, when navigating to a destination.…
Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
Pilar Bachiller-Burgos, Ulysses Bernardet, Luis V. Calderita +8
This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization for ground robots. We…
CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models
Catherine Glossop, William Chen, Arjun Bhorkar +2
Generalist robots should be able to understand and follow user instructions. Despite providing a powerful architecture for mapping open-vocabulary language instructions to robot ac…
Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization
Jonathan Yang, Chuyuan Kelly Fu, Dhruv Shah +3
In this work, we investigate how spatially grounded auxiliary representations can provide both broad, high-level grounding as well as direct, actionable information to improve poli…
Guiding Data Collection via Factored Scaling Curves
Lihan Zha, Apurva Badithela, Michael Zhang +7
Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different condition…