DeepMPC: Learning Deep Latent Features for Model Predictive Control

Lenz, Ian; Knepper, Ross A.; Saxena, Ashutosh

doi:10.15607/rss.2015.xi.012

Cited by 288 publications

(228 citation statements)

References 28 publications

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“…Previous work on robotic cutting has focused on modelpredictive control (MPC) to generalize between different types of food items. Lenz et al [3] proposed learning a deep network to model the cutting interaction's dynamics for their DeepMPC approach. This network continually estimated the latent material properties throughout the cutting process using interactive perception and a recurrent network structure.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

Zhang

Sharma

Veloso

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly.In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events. The robot learns neural networks for performing each of these tasks and generalizing across different material properties. By adapting and monitoring the skill executions, the robot is able to reliably cut through more than 20 different types of food items and even detect whether certain food items are fresh or old.2 Manuela Veloso is with the Machine Learning

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Section: Related Workmentioning

confidence: 99%

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

Zhang

Sharma

Veloso

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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“…Recent papers have explored model-predictive control with deep networks [21], [22], [23], [24], [25]. These approaches learn an abstract-state transition function, not an explicit model of the environment [26], [27].…”

Section: Control With a Learned Simulatormentioning

confidence: 99%

Combining Physical Simulators and Object-Based Networks for Control

Ajay

Bauzá

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner. Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.

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“…Lenz, Knepper, and Saxena [122] modeled robotic food cutting with a knife. This includes difficult-to-model effects such as friction, deformation, and hysteresis.…”

Section: Examples In Recent Researchmentioning

confidence: 99%

“…This multi-step approach sped both training and classification at runtime. Lenz et al [122] employed a two-stage network design for grasp detection. The first DNN had relatively few parameters.…”

Section: Current Shortcomings Of Dnns For Roboticsmentioning

confidence: 99%

Deep learning in robotics: a review of recent research

2017

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Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.

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DeepMPC: Learning Deep Latent Features for Model Predictive Control

Cited by 288 publications

References 28 publications

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

Combining Physical Simulators and Object-Based Networks for Control

Deep learning in robotics: a review of recent research

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