An In Depth View of Saliency
- A. Ciptadi, Tucker Hermans, James M. Rehg
- EconomicsBritish Machine Vision Conference
- 1 September 2013
Presented at the 24th British Machine Vision Conference (BMVC 2013), 9-13 September 2013, Bristol, UK.
Affordance Prediction via Learned Object Attributes
- Tucker Hermans, James M. Rehg, A. Bobick
- Psychology
- 2011
We present a novel method for learning and predicting the affordances of an object based on its physical and visual attributes. Affordance prediction is a key task in autonomous robot learning, as it…
Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network
- Qingkai Lu, Kautilya Chenna, Balakumar Sundaralingam, Tucker Hermans
- Computer ScienceInternational Symposium of Robotics Research
- 10 April 2018
This work is the first to directly plan high quality multifingered grasps in configuration space using a deep neural network without the need of an external planner and shows that the planning method outperforms existing planning methods for neural networks.
Decoupling behavior, perception, and control for autonomous learning of affordances
- Tucker Hermans, James M. Rehg, A. Bobick
- Computer ScienceIEEE International Conference on Robotics and…
- 6 May 2013
This work demonstrates the approach using a PR2 robot that explores different combinations of controller, behavior primitive, and proxy to perform a push or pull positioning behavior on a selection of household objects, learning which methods best work for each object.
Movie genre classification via scene categorization
- Howard Zhou, Tucker Hermans, Asmita V. Karandikar, James M. Rehg
- Computer ScienceACM Multimedia
- 25 October 2010
This paper presents a method for movie genre categorization of movie trailers, based on scene categorization, and demonstrates that exploiting scene structures improves film genre classification using only low-level visual features.
Stabilizing novel objects by learning to predict tactile slip
- Filipe Veiga, H. V. Hoof, Jan Peters, Tucker Hermans
- Computer ScienceIEEE/RJS International Conference on Intelligent…
- 17 December 2015
This work explores the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors in the feedback loop of an object stabilization controller and shows that the controller can successfully stabilize previously unknown objects by predicting and counteracting slip events.
Learning contact locations for pushing and orienting unknown objects
- Tucker Hermans, Fuxin Li, James M. Rehg, A. Bobick
- PsychologyIEEE-RAS International Conference on Humanoid…
- 1 October 2013
With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class.
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
- Mark Van der Merwe, Qingkai Lu, Balakumar Sundaralingam, Martin Matak, Tucker Hermans
- Computer ScienceIEEE International Conference on Robotics and…
- 2 October 2019
This work proposes to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system, and leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
Modeling Grasp Type Improves Learning-Based Grasp Planning
- Qingkai Lu, Tucker Hermans
- Computer ScienceIEEE Robotics and Automation Letters
- 10 January 2019
This paper proposes a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real time and shows the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.
Learning robot in-hand manipulation with tactile features
- H. V. Hoof, Tucker Hermans, G. Neumann, Jan Peters
- Computer Science, PsychologyIEEE-RAS International Conference on Humanoid…
- 28 December 2015
This approach successfully acquires a tactile manipulation skill using a passively compliant hand and it is shown that the learned tactile skill generalizes to novel objects.
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