Shakey the Robot
By Nils J. Nilsson. 1984
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sergey Levine
- Yoshua Bengio
- Pieter Abbeel
- Trevor Darrell
- Abhinav Kumar Gupta
- Silvio Savarese
- Jitendra Malik
- Josh Tenenbaum
Citations in the corpus
- The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation (2018, background): “Robot visual navigation has a long history [15], [16], [17], [18], [19].”
- Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion (2021, background): “Robot agents that navigate and perform object manipulation in human spaces have been a long-range goal of AI researchers for decades [53].”
- Evolutionary Robotics and the Radical Envelope-of-Noise Hypothesis (1997, background): “This latter fact has fueled the trend in mobile robotics over the last few years from the internal world model-making robots of the seventies (Nilsson, 1984) to the current low-level behavior-based robotics of the present day (Chiel, Beer, Quinn, & Espenschied, 1992) for patient proofreading and…”
- Teleo-Reactive Programs for Agent Control (1993, methodology): “For those applications, the T-R level shields the programmer from having to worry about how that level is implemented and greatly facilitates program construction.”
- Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms (2018, unknown): “Early systems such as Winograd’s SHRLDU (Wino-grad, 1971) and Nilsson’s Shakey (Nilsson, 1984) demonstrated basic command following and reasoning with manually prescribed connections between language and symbols that generated control signals.”
- Vision for Mobile Robot Navigation: A Survey (2002, methodology): “From the pioneering robotic vehicle work by Giralt et al. in 1979, [44], and later by Moravec in 1980 and 1983, [107], [108] and Nilsson in 1984, [114], it became clear that, implicitly or explicitly, it was imperative for a vision system meant for navigation to incorporate within it some knowledge…”
- Embodied Cognition: A field guide (2003, background): “, Skakey [87], CART [84] and Hilare [47], he comments:”
- Introduction to Autonomous Mobile Robots (2004, background): “It is interesting to note the similarity between this general architecture, used in many specialized forms in mobile robotics today, and the architecture implemented by Shakey, one of the very first mobile robots, in 1969 [115].”
- Integrated Task and Motion Planning (2020, methodology): “n and any soft action cost penalties, using local-descent methods, though these are guaranteed to reach only a local optimum of the objective function, which may not satisfy the constraints. Equation (2) displays a mathematical program corresponding to the constraint network in Figure 9. The traject”
- A review of learning planning action models (2018, methodology): “The STRIPS (STanford Research Institute Problem Solver) system (Fikes & Nilsson, 1971) was used to control and plan the movement of an autonomous robot called Shakey (Nilsson, 1984) as it pushed a series of boxes through a series of interconnected rooms.”
- Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning (2022, background): “Another clear limitation of early work on abstractions is the reliance on manual specification [7, 8, 11].”
- Cognitive Architectures for Language Agents (2023, background): “Physical embodiment is the oldest instantiation envisioned for AI agents (Nilsson, 1984).”
- Extrinsic dexterity: In-hand manipulation with external forces (2014, unknown): “The following subsections explore those three categories.”
- The Artificial Life Roots of Artificial Intelligence (1993, background): “If intelligent robots have been considered (as in Nilsson [90]), sensing and action has been delegated to subsystems that are assumed to deliver symbolic descriptions to the central planning and decision-making modules.”
- From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning (2018, methodology): “…of control so that an agent can learn and plan using higher-level skills, echoing and modeling similar approaches in robotics based on building and sequencing a collection of low-level motor controllers (Nilsson, 1984; Malcolm & Smithers, 1990; Brooks, 1991; Huber & Grupen, 1997; Gat, 1998).”