Interpreting and Executing Recipes with a Cooking Robot

By M. Bollini, Stefanie Tellex, Tyler Thompson, Nicholas A. Roy, Daniela Rus. 2012

165 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Natural Language Processing, Reinforcement Learning

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Citations in the corpus

Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Some works utilize language for planning and grounding [39, 40, 41, 42, 43, 44].”
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (2019, background): “Instruction following is a long-standing topic of interest in robotics [7, 10, 29, 34, 39, 40, 46, 51].”
Learning human activities and object affordances from RGB-D videos (2012, background): “Robots are now becoming more integrated in human environments and are being used in assistive tasks such as automatically interpreting and executing cooking recipes (Bollini et al., 2012), robotic laundry folding (Miller et al.”
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020, background): “Robotic manipulation of deformable objects has a rich history across various ﬁelds, such as folding laundry [1], preparing food [18], or assistive dressing and feeding [19, 20].”
Gated-Attention Architectures for Task-Oriented Language Grounding (2017, background): “Some of the work that aims to ground navigational instructions include Guadarrama et al. (2013), Bollini et al. (2013) and Beetz et al. (2011), where the focus was to ground verbs like go, follow, etc and spatial relations of verbs (Tellex et al., 2011; Fasola and Mataric, 2013).”
CLIPort: What and Where Pathways for Robotic Manipulation (2021, background): “While language-grounding for manipulation has been explored in the past [7, 8, 9, 10], these pipelines are limited by object-centric representations that cannot handle granular or deformable objects and often do not reason about perception and action in an integrated manner.”
Asking for Help Using Inverse Semantics (2014, background): “Robotic capabilities such as robust manipulation, accurate perception, and fast planning algorithms have led to recent successes such as robots that can fold laundry [MaitinShepard et al., 2010], cook dinner [Bollini et al., 2012], and assemble furniture [Knepper et al., 2013].”
Robots That Use Language (2020, background): “(152) created a system for interpreting recipes but did not ground ingredients into perception.”
Episodic Transformer for Vision-and-Language Navigation (2021, background): “Building systems to understand and execute human instructions has been the subject of many previous works [7, 8, 10, 12, 37, 41, 46, 47, 52, 62].”
Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions (2014, methodology): “Other than the works discussed in the introduction (Beetz et al., 2011; Bollini et al., 2012; Guadarrama et al., 2013; Marco et al., 2012), the problem of navigation has been addressed by using learned models for verbs such as follow, meet, go, and dynamic spatial relations such as walk close to…”
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models (2023, unknown): “Prior works have leveraged lexical analysis to parse the instructions [6–8], while more recently language models have been used to decompose the instructions into a textual sequence of steps [9–11].”
Cooking with Semantics (2014, background): “In the domain of food recipes, there have already been several demonstrations (e.g., (Beetz et al., 2011; Bollini et al., 2013)) of robots automatically cooking meals based on recipes.”
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022, methodology): “Several works have proposed methods for grounding language in robot actions [49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60].”