Reconstructing the robot’s state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which processes on-board sensor information to learn a ...
DOI: / Corpus ID: 231973471. Co-Learning of Task and Sensor Placement for Soft Robotics @article{Spielberg2021CoLearningOT, title={Co-Learning of Task and Sensor Placement for Soft Robotics}, author={Andrew Spielberg and Alexander Amini and Lillian Chin and W. Matusik and D. Rus}, journal={IEEE Robotics and Automation Letters}, year={2021}, volume={6}, …
Co-Learning of Task and Sensor Placement for Soft Robotics. A Spielberg, A Amini, L Chin, W Matusik, D Rus. IEEE Robotics and Automation Letters 6 (2), 1208-1215, 2021. 4: 2021: Deep evidential regression . A Amini, W Schwarting, A Soleimany, D Rus. Advances in Neural Information Processing Systems (NeurIPS) 33, 2020. 23: 2020: Uncertainty Aware Texture Classification and Mapping Using Soft ...
Reconstructing the robot's state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which ...
· Andrew Spielberg, Alexander Amini, Lillian Chin, Wojciech Matusik, Daniela Rus. Co-Learning of Task and Sensor Placement for Soft Robotics. IEEE Robotics and Automation Letters , 2021; 6 (2): 1208 ...
Reconstructing the robot’s state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which processes on-board sensor information to learn a ...
Columns represent different latent dimensions of the elephant, generated by one-hot latent vector activations. Each column ranges latent activations to Redder particles indicate higher speeds in x; bluer particles indicate higher speeds in y. - "Co-Learning of Task and Sensor Placement for Soft Robotics"
· “A robot with rigid hands will have much more trouble with tasks like picking up an object,” Homberg says. “This is because it has to have a good model of the object and spend a lot of time thinking about precisely how it will perform the grasp.” Soft robots represent an intriguing new alternative. However, one downside to their extra ...
Fig. 3. Results for object classification ((a)(d)) and stiffness regression ((e)(g)) tasks. (a) Convergence of the loss for = 5, 10, 20 on the sparifying algorithm, with comparison to baselines and full sensorization. (b) Confusion matrices for our co-learning algorithm across different sensor budgets. (c) Evolution of sensor probabilities over training time, validating that each i converges ...
Co-Learning of Task and Sensor Placement for Soft Robotics A Spielberg*, A Amini*, L Chin, W Matusik, D Rus IEEE Robotics and Automation Letters 6 (2), 1208-1215 , 2021
Co-Learning of Task and Sensor Placement for Soft Robotics. Andrew Spielberg, Alexander Amini, Lillian Chin, Wojciech Matusik, Daniela Rus. Co-Learning of Task and Sensor Placement for Soft Robotics. IEEE Robotics and Automation Letters, 6(2): 1208-1215, 2021.
· Unlike rigid robots which operate with compact degrees of freedom, soft robots must reason about an infinite dimensional state space. Mapping this continuum ...
· Reconstructing the robot’s state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which processes on-board sensor information to learn a ...
· Co-Learning of Task and Sensor Placement for Soft RoboticsSupplementary VideoAuthors:Andrew Spielberg*, Alexander Amini*, Lillian Chin, Wojciech Matusik, Dan...
Reconstructing the robot's state from these sparse inputs is challenging, especially since sensor location has a profound downstream impact on the richness of learned models for robotic tasks. In this work, we present a novel representation for co-learning sensor placement and complex tasks. Specifically, we present a neural architecture which processes on-board sensor information to learn a ...
· 22《IEEE》，“Co-Learning of Task and Sensor Placement for Soft Robotics”()，Andrew Spielberg。 ，， ...
· Co-Learning of Task and Sensor Placement for Soft Robotics. What benefits come with ensuring accurate sensor placement? Our soft robots can learn to solve specific desired tasks more optimally by placing sensors in task-relevant locations. For example, we demonstrate improved ability to identify the shapes of objects and the stiffness of objects grasped by a soft gripper, reconstruct the …
· Co-Learning of Task and Sensor Placement for Soft Robotics. Abstract: Unlike rigid robots which operate with compact degrees of freedom, soft robots must reason about an infinite dimensional state space. Mapping this continuum state space presents significant challenges, especially when working with a finite set of discrete sensors.
· That’s a tall task for a soft robot that can deform in a virtually infinite number of ways. MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and ...
· Co-Learning of Task and Sensor Placement for Soft Robotics. Published by Robot Tracker at April 2, 2021. Categories . Robot News; Tags . Robotics; Deep-learning technique optimizes the arrangement of sensors on a robot’s body to ensure efficient operation. Source: Robotics Tomorrow. Related posts. June 15, 2021 . Scientists make highly maneuverable miniature robots …
· MIT researchers developed a deep learning neural network to aid the design of soft-bodied robots. The algorithm optimizes the arrangement of sensors on the robot, enabling it to complete tasks …
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