Mark Steedman
Papers
11
Total Citations
324
H-Index
8
About
Mark Steedman is a prominent researcher whose work bridges the critical gap between high-level artificial intelligence planning and low-level robotic control, with significant contributions to cognitive robotics, knowledge representation, and computational linguistics. His most influential contribution, the Object-Action Complex (OAC) framework — introduced in collaborative work that has garnered over 150 citations — provides a principled formalism for grounding symbolic representations in sensorimotor processes, enabling robots to reason about and execute actions in real-world environments. This foundational concept underpins much of his subsequent research, including efforts to integrate cognitive vision systems, automated planning, and execution monitoring on humanoid robot platforms. Steedman has also advanced the field by exploring how common-sense knowledge can be extracted from text to support autonomous robot planning, and by developing machine learning approaches — such as kernel perceptron models — to help robots acquire action knowledge from experience. His 2018 ACL Lifetime Achievement Award, celebrated in "The Lost Combinator," underscores his broader impact on computational linguistics. Across his career, Steedman has consistently tackled the representational discontinuities that challenge truly autonomous, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Object–Action Complexes: Grounded abstractions of sensory–motor processes153 citations · 2011
- 2Object Action Complexes as an Interface for Planning and Robot Control66 citations · 2006
- 3Extracting common sense knowledge from text for robot planning24 citations · 2014
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- 6Using Kernel Perceptrons to Learn Action Effects for Planning18 citations · 2008
- 7Grounded spatial symbols for task planning based on experience12 citations · 2013
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- 10The Lost Combinator2 citations · 2018