Planning and Control with Imperfect Sensors and Perception
Planning and Control with Imperfect Sensors and Perception
In conjunction with
Robotics: Science and Systems 2026
July 13-17, 2026
University of Technology Sydney
Sydney, Australia
Examines how autonomous robots can make decisions when their perception systems are imperfect and uncertain, focusing on integrating modern semantic and vision-language models into planning and control pipelines. Addresses tightly coupled perception, planning, and control where robots must operate with high-dimensional semantic representations rather than precise state estimates.
More workshops at Robotics: Science and Systems 2026
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- It's the Demos: A Deep Look at the Role of Demonstration Quality in Imitation-Based Robot Manipulation — Examines how demonstration quality fundamentally shapes imitation-based robot manipulation systems. Brings together researchers to establish...
- 4th Workshop on Dexterous Manipulation: Scalable Learning for Human-Level Skills — Focuses on scaling dexterous manipulation by leveraging advances in robotic hand hardware, large-scale video datasets,...
- Diffusion for Robot Learning — Explores how diffusion models can be applied to robot learning for both data generation and...
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- Reason, Plan, Act: A Hands-On Tutorial on Embodied Agentic AI — Teaches participants how to build autonomous AI systems that combine large language models with robotics,...
- Failure Is Not the End: Towards Open-World Failure Recovery for Robots — Addresses how robots can detect, diagnose, and recover from failures when operating in open-world environments...
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- Rethinking What It Means to be Safe for Generalist Robots — Addresses how to redefine safety for generalist robots that operate across diverse tasks and environments,...
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- Human-centric Mobile Manipulation Workshop — Addresses how mobile manipulation robots can effectively operate alongside humans in real-world environments by developing...
- From Imitation to Certification: Learning, Reasoning, and Formal Methods for Safe Robot Manipulation — Brings together researchers from imitation learning, AI, control theory, and formal verification to address safety...
- Lab-to-Production Workshop: Toward Industrial-Grade Perception and Manipulation — Addresses the gap between robotics research and industrial deployment by examining how to translate advances...
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- Post-training for Robotics Foundation Models — Examines how robotics foundation models can improve beyond initial pretraining by collecting their own interaction...
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- Reinforcement Learning for Vision-Language-Action Models (RL4VLA) — Addresses how reinforcement learning can improve Vision-Language-Action models for robotics by moving beyond imitation learning...
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- Closing the Sim2Real Gap: Progress and Challenges — Addresses the research challenge of bridging the gap between simulation and real-world robotics implementation. Examines...
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- Tactile Sensing for Robotic Foundation Models — Explores how tactile sensing can be integrated into robotic foundation models at scale, addressing hardware...
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- Trustworthy Embodied Foundation Models: Safety by Design versus Safety by Practice — Addresses safety challenges of deploying foundation models in robotic systems, exploring safety by design through...
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- Beyond the Lab: Rethinking Methods for Human Behavior Monitoring and Modeling in In-the-Wild Human-Robot Interaction — Examines methodologies for observing and understanding human behavior in real-world robotic deployments, addressing the challenge...