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Robots operating in human environments must be capable of interacting with a wide variety of articulated objects such as cabinets, refrigerators, and drawers. Existing approaches require human demonstration or minutes of interaction to fit kinematic models to each novel object from scratch. We present a framework for estimating the kinematic model and configuration of previously unseen articulated objects, conditioned upon object type, from as little as a single observation. We train our system in simulation with a novel dataset of synthetic articulated objects; at runtime, our model can predict the shape and kinematic model of an object from depth sensor data. We demonstrate that our approach enables a MOVO robot to view an object with its RGB-D sensor, estimate its motion model, and use that estimate to interact with the object.
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Generation of Novel Fall Animation with Configurable Attributes
It takes less than half a second for a person to fall [8]. Capturing the essence of a fall from video or motion capture is difficult. More generally, generating realistic 3D human body motions from motion capture (MoCap) data is a significant challenge with potential applications in animation, gaming, and robotics. Current motion datasets contain single-labeled activities, which lack fine-grained control over the motion, particularly for actions as sparse, dynamic, and complex as falling. This work introduces a novel human falling dataset and a learned multi-branch, Attribute-Conditioned Variational Autoencoder model to generate novel falls. Our unique dataset introduces a new ontology of the motion into three phases: Impact, Glitch, and Fall. Each branch of the model learns each phase separately and the fusion layer learns to fuse the latent space together. Furthermore, we present data augmentation techniques and an inter-phase smoothness loss for natural plausible motion generation. We successfully generated high-quality images, validating the efficacy of our model in producing high-fidelity, attribute-conditioned human movements.
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- Award ID(s):
- 2318255
- PAR ID:
- 10538839
- Publisher / Repository:
- ACM
- Date Published:
- Edition / Version:
- 1
- ISBN:
- 9798400709944
- Page Range / eLocation ID:
- 1 to 6
- Subject(s) / Keyword(s):
- Motion Synthesis, Human Body, VAE, Dataset, Fall
- Format(s):
- Medium: X Size: 4MB Other: pdf
- Size(s):
- 4MB
- Location:
- Utrecht Netherlands
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3658852.3659087
