Simon Stepputtis

Simon Stepputtis

Assistant Professor | Virginia Tech

  • I will start as Assistant Professor at Virginia Tech in Fall 2025

    [] In my work, I create intelligent robots and systems that can effectively learn, autonomosly adapt to, and operate in unstructured human-centric environments. Find out more on my Research Page

    • Paper: ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition

      [] A novel method enables robots to intuitively grasp unfamiliar objects by decomposing their shapes and utilizing large language models, achieving high success rates in experimental trials. Paper on arXiv

    • Paper: A Comparison of Imitation Learning Algorithms for Bimanual Manipulation

      [] Explore how different imitation learning algorithms tackle complex industrial tasks, revealing key strengths and weaknesses in precision, efficiency, and adaptability. Paper on arXiv

    • University of Washington: Invited Talk

      [] I am excited to give a talk at the University of Washington about Neuro-Symbolic Robot Intelligence!

    • Multiple ICRA Workshop Papers!

      [] I will be at ICRA 2024 to present some of our most recent work. Check out the Publications!.

    • Multiple New Papers (NeurIPS, EMNLP, CoLLAs, AURO, CVPR)

      [] I updated the website with multiple new papers, including EMNLP 2023, NeurIPS 2023, and CVPR 2024, CoLLAs 2024 and the Autonomous Robots Journal.

    • Paper: Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation

      [] Introducing Sigma, a groundbreaking Siamese Mamba network that revolutionizes AI scene understanding by combining thermal, depth, and RGB data for more accurate predictions in challenging environments. Paper on arXiv

    • Paper: Sample-Efficient Learning of Novel Visual Concepts

      [] Sample-efficient extraction of novel objects, affordances, and attributes from images using symbolic domain knowledge, which will be presented at CoLLAs 2023

    • Paper: Introspective Action Advising for Interpretable Transfer Learning

      [] We propose an alternative approach to transfer learning between tasks based on action advising, which will be presented at CoLLAs 2023!

    • RSS 2023: Articulate Robots Workshop

      [] I am organizing a workshop at RSS 2023 in Daegu, Republic of Korea on Articulate Robots: Utilizing Language for Robot Learning

    • Paper: Explainable Action Advising for Multi-Agent Reinforcement Learning

      [] Our new paper will be presented at ICRA 2023 in London, England! Paper on arXiv

    • Paper: Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation

      [] Our new paper with our collaborators at Intel will be presented at CORL 2022 in Auckland, New Zeland! Paper on OpenReview

    • Paper: Concept Learning for Interpretable Multi-Agent Reinforcement Learning

      [] Interpretable concept learning for multi-agent robot systems: CORL 2022 in Auckland, New Zeland! Paper on OpenReview

    • Paper: A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies

      [] Our paper in collaboration with Intrinsic got accepted to IROS 2022, which we will be presenting in Kyoto, Japan. View full paper

    • IROS 2022: TOM4HAT Workshop

      [] I organized a workshop at IROS 2022 in Kyoto, Japan on Theory of Mind

    • Workshop: RSS Pioneers

      [] I was accepted to the RSS Pioneers Workshop 2022 with my work on Language-Conditioned Human-Agent Teaming.

    • Postdoctoral Fellow at Carnegie Mellon University

      [] I started as a postdoctoral fellow at Carnegie Mellon University (CMU) with Prof. Katia Sycara.

    • Graduation: Ph.D. in Computer Science

      [] I completed my Ph.D. in Computer Science at Arizona State University with Prof. Heni Ben Amor!

    • Imperial College London: Invited Talk

      [] I will be giving brief summary and outlook of my work presented in our NeurIPS 2020 paper at the Imperial College London!

    • Resident @ X, The Moonshot Factory

      [] Over the summer, I will be a resident at X, The Moonshot Factory, where I will be working on industrial manipulation tasks for Intrinsic, a robotics software and AI project at X .

    • Video: Language Conditioned Imitation Learning

      [] We contributed a video to the robot expo at IJCAI 2021 that is a direct extension to our NeurIPS 2020 paper. You can check out the video here!

    • Paper: Language-Conditioned Imitation Learning for Robot Manipulation Tasks

      [] We published a new paper at NeurIPS 2020! Our paper got accepted as a spotlight presentation (top ~4% of accepted papers). View full paper

    • Intel AI Labs: Invited Talk

      [] I am excited to give a talk, Language for Robotics at Intel AI Labs summarizing our efforts on learning robot policies from natural language instructions.

    • Teaching Introduction to Theoretical Computer Science at ASU

      [] I will be teaching CSE 355: Introduction to Theoretical Computer Science at Arizona State University as the main instructor during the upcoming Summer 2020 semester!

    • Intel AI: Talk at the Deep Learning Community

      [] I will be giving a talk at the Deep Learning Community of Practice titled Imitation Learning for Adaptive Robot Control Policies from Language, Vision, and Motion.

    • Workshop Paper: Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration

      [] We contributed a workshop paper to the Workshop on Robot Learning at NeurIPS 2019!

    • Best Poster Award

      [] I received the Best Poster Award by Nvidia at the Southwest Robotics Symposium for my work on Neural Policy Translation for Robot Control!

    • Robotics Intern @BOSCH

      [] I will be joining Robot BOSCH in Sunnyvale for an internship to work on semantic data analysis with a focus on time series segmentation.

    • Paper: Extrinsic Dexterity through Active Slip Control using Deep Predictive Models

      [] We got our paper accepted to ICRA 2018, and I will be presenting our work in Brisbane, Australia! Paper on IEEE Xplore

    • Best Video Award

      [] Awarded at the International Conference on Humanoid Robots (Humanoids) 2016 for our work on Learning human-robot interactions from human-human demonstrations Video on YouTube