Max Robotics — Global Service Robot Directory + US Certification

About

Jonathan Tompson is a prominent robotics and machine learning researcher whose work sits at the intersection of embodied AI, robotic manipulation, and large language models. His research has consistently pushed the boundaries of how robots perceive, reason about, and interact with the physical world. Tompson's most influential contributions include co-developing PaLM-E (2023, 350 citations), a landmark embodied multimodal language model that bridges real-world sensory input with the reasoning power of large language models. This work, alongside Inner Monologue (2022, 206 citations), has been central to establishing how LLMs can enable sophisticated planning and feedback loops in robotic systems. His Transporter Networks framework (2020, 100 citations) introduced an elegant architecture for spatial reasoning in manipulation tasks, later extended to challenging deformable objects like cables, fabrics, and bags (2021, 122 citations). Earlier foundational work on granular media manipulation (2017) and actionable visual representations (2018) demonstrates Tompson's long-standing commitment to grounding robot learning in real-world complexity. His Interactive Language framework (2024) further advances real-time, natural language-instructable robotics. With over 850 cumulative citations and contributions spanning perception, planning, and language-conditioned control, Tompson represents a leading voice shaping the future of general-purpose robotic intelligence.