Justus Piater

About

Justus Piater is a prominent researcher at the intersection of robotics, computer vision, and cognitive science, whose work has fundamentally advanced how robots perceive, learn, and interact with the physical world. Best known for his extensive contributions to the theory and application of affordances in robotics — a concept he has helped define and systematize through highly influential survey papers (164 and 81 citations) — Piater has shaped how the field understands the relationship between perception and action in artificial systems. His research on robot grasping is particularly impactful, with pioneering work on learning grasp affordance densities using probabilistic models (156, 80, and 70 citations) that enable robots to generalize grasping knowledge across novel objects. Through the framework of Object-Action Complexes (153 citations), he has contributed foundational abstractions bridging sensorimotor experience and symbolic reasoning, a theme further extended in his work on bottom-up learning for symbolic planning (79 citations) and assembly task semantics (64 citations). Piater's research elegantly spans from low-level haptic and visual perception in humanoid robots (107 citations) to high-level ontologies of manipulation actions (87 citations), reflecting a career dedicated to building robots capable of truly understanding and acting meaningfully within human environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Affordances in Psychology, Neuroscience, and Robotics: A Survey

   164 citations · 2016
2. 2
   Combining active learning and reactive control for robot grasping

   156 citations · 2010
3. 3
   Object–Action Complexes: Grounded abstractions of sensory–motor processes

   153 citations · 2011
4. 4
   Developing haptic and visual perceptual categories for reaching and grasping with a humanoid robot

   107 citations · 2001
5. 5
   A Simple Ontology of Manipulation Actions Based on Hand-Object Relations

   87 citations · 2013
6. 6
   Computational models of affordance in robotics: a taxonomy and systematic classification

   81 citations · 2017
7. 7
   Learning Grasp Affordance Densities

   80 citations · 2011
8. 8
   Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning

   79 citations · 2015
9. 9
   Learning object-specific grasp affordance densities

   70 citations · 2009
10. 10
    Teaching a Robot the Semantics of Assembly Tasks

    64 citations · 2017

Key Collaborators

Contact & Links