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Pranav Putta

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NeurIPS Conference 2025 Conference Paper

REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites

  • Div Garg
  • Diego Caples
  • Andis Draguns
  • Nikil Ravi
  • Pranav Putta
  • Naman Garg
  • Prannay Hebbar
  • Youngchul Joo

We introduce REAL, a benchmark and framework for multi-turn agent evaluations on deterministic simulations of real-world websites. REAL comprises high-fidelity, deterministic replicas of 11 widely-used websites across domains such as e-commerce, travel, communication, and professional networking. We also release a benchmark consisting of 112 practical tasks that mirror everyday complex user interactions requiring both accurate information retrieval and state-changing actions. All interactions occur within this fully controlled setting, eliminating safety risks and enabling robust, reproducible evaluation of agent capability and reliability. Our novel evaluation framework combines programmatic checks of website state for action-based tasks with rubric-guided LLM-based judgments for information retrieval. The framework supports both open-source and proprietary agent systems through a flexible evaluation harness that accommodates black-box commands within browser environments, allowing research labs to test agentic systems without modification. Our empirical results show that frontier language models achieve at most a 41% success rate on REAL, highlighting critical gaps in autonomous web navigation and task completion capabilities. Our framework supports easy integration of new tasks, reproducible evaluation, and scalable post-training data generation, marking a significant step forward in evaluating and advancing agent capabilities.

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IROS Conference 2024 Conference Paper

Embodiment Randomization for Cross Embodiment Navigation

  • Pranav Putta
  • Gunjan Aggarwal
  • Roozbeh Mottaghi
  • Dhruv Batra
  • Naoki Yokoyama
  • Joanne Truong
  • Arjun Majumdar

We present Embodiment Randomization, a simple, inexpensive, and intuitive technique for training robust behavior policies that can be transferred to multiple robot embodiments. While prior works require real-world data from multiple robots, or complex algorithmic adjustments to address the challenge of embodiment generalization, our approach leverages the power of simulation and large-scale reinforcement learning and can be easily integrated within existing policy learning methods. We show that policies trained with embodiment randomization implicitly perform system identification, enabling them to adapt to new embodiments during deployment. Our approach not only shows significant improvements in adapting to novel robot configurations, but also in generalizing from simulation to reality and contending with real-world perturbations, highlighting the potential of embodiment randomization in creating versatile and adaptable robotic navigation policies.

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