Savarese, Silvio

OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., Hua, T. J., Cheng, Z., Shin, D., Lei, F., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 64

iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

Li, C., Xia, F., Martín-Martín, R., Lingelbach, M., Srivastava, S., Shen, B., Vainio, K., Gokmen, C., Dharan, G., Jain, T., et al. (2021) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 58

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments

Srivastava, S., Li, C., Lingelbach, M., Martín-Martín, R., Xia, F., Vainio, K., Lian, Z., Gokmen, C., Buch, S., Liu, C. K., et al. (2021) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 55

Learning Multi-Arm Manipulation Through Collaborative Teleoperation

Tung, A., Wong, J., Mandlekar, A., Martín-Martín, R., Zhu, Y., Fei-Fei, L., & Savarese, S. (2020) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 49

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity

Mandlekar, A., Booher, J., Spero, M., Tung, A., Gupta, A., Zhu, Y., Garg, A., Savarese, S., & Fei-Fei, L. (2019) · arXiv

Robot Manipulation and LearningTeleoperation and Haptic Systems

Cited by 48

Multimodal Sensor Fusion with Differentiable Filters

Lee, M. A., Yi, B., Martin-Martin, R., Savarese, S., & Bohg, J. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Target Tracking and Data Fusion in Sensor NetworksRobot Manipulation and Learning

Cited by 46

Human-in-the-Loop Imitation Learning using Remote Teleoperation

Mandlekar, A., Xu, D., Martín-Martín, R., Zhu, Y., Fei-Fei, L., & Savarese, S. (2020) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 35

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Xia, F., Li, C., Martín-Martín, R., Litany, O., Toshev, A., & Savarese, S. (2020) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 33

ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 21

Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation

Wong, J., Tung, A., Kurenkov, A., Mandlekar, A., Fei-Fei, L., Savarese, S., & Martín-Martín, R. (2021) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 19

Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Mandlekar, A., Xu, D., Martín-Martín, R., Savarese, S., & Fei-Fei, L. (2020) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 17

Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets

Liu, Z., Hoang, T., Zhang, J., Zhu, M., Lan, T., Kokane, S., Tan, J., Yao, W., Liu, Z., Feng, Y., et al. (2024) · Advances in Neural Information Processing Systems 37

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 13

APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

Liu, Z., Hoang, T., Zhang, J., Zhu, M., Lan, T., Kokane, S., Tan, J., Yao, W., Liu, Z., Feng, Y., et al. (2024) · arXiv

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 13

BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents

Liu, Z., Yao, W., Zhang, J., Xue, L., Heinecke, S., Murthy, R., Feng, Y., Chen, Z., Niebles, J. C., Arpit, D., et al. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 11

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

Yao, W., Heinecke, S., Niebles, J. C., Liu, Z., Feng, Y., Xue, L., Murthy, R., Chen, Z., Zhang, J., Arpit, D., et al. (2023) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 10

AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System

Liu, Z., Yao, W., Zhang, J., Yang, L., Liu, Z., Tan, J., Choubey, P. K., Lan, T., Wu, J., Wang, H., et al. (2024) · arXiv

AI-based Problem Solving and PlanningNatural Language Processing Techniques

Cited by 10

BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation

Li, C., Zhang, R., Wong, J., Gokmen, C., Srivastava, S., Martín-Martín, R., Wang, C., Levine, G., Ai, W., Martinez, B., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 6

A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

Ke, Z., Jiao, F., Ming, Y., Nguyen, X.-P., Xu, A., Long, D. X., Li, M., Qin, C., Wang, P., Savarese, S., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Luo, Z., Shen, Z., Yang, W., Zhao, Z., Jwalapuram, P., Saha, A., Sahoo, D., Savarese, S., Xiong, C., & Li, J. (2025) · arXiv

Topic ModelingArtificial Intelligence Applications

Cited by 3

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

Prabhakar, A., Liu, Z., Zhu, M., Zhang, J., Awalgaonkar, T., Wang, S., Liu, Z., Chen, H., Hoang, T., Niebles, J. C., et al. (2025) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 2

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents

Zhang, K., Yao, W., Liu, Z., Feng, Y., Liu, Z., Murthy, R., Lan, T., Li, L., Lou, R., Xu, J., et al. (2024) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 1

Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding

Chen, H., Feng, Y., Liu, Z., Yao, W., Prabhakar, A., Heinecke, S., Ho, R., Mui, P., Savarese, S., Xiong, C., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

DarwinX: Evolving Agent Harnesses Through Natural Selection

Zhang, Y., Dai, Y., Tan, J., Yang, L., Mullur, R., Hoang, T., Hu, Z., Zhu, J., Mui, P., Savarese, S., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 1

Reward-Guided Speculative Decoding for Efficient LLM Reasoning

Liao, B., Xu, Y., Dong, H., Li, J., Monz, C., Savarese, S., Sahoo, D., & Xiong, C. (2025) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 1