5 research outputs found
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation
Developing robot controllers in a simulated environment is advantageous but
transferring the controllers to the target environment presents challenges,
often referred to as the "sim-to-real gap". We present a method for continuous
improvement of modeling and control after deploying the robot to a
dynamically-changing target environment. We develop a differentiable physics
simulation framework that performs online system identification and optimal
control simultaneously, using the incoming observations from the target
environment in real time. To ensure robust system identification against noisy
observations, we devise an algorithm to assess the confidence of our estimated
parameters, using numerical analysis of the dynamic equations. To ensure
real-time optimal control, we adaptively schedule the optimization window in
the future so that the optimized actions can be replenished faster than they
are consumed, while staying as up-to-date with new sensor information as
possible. The constant re-planning based on a constantly improved model allows
the robot to swiftly adapt to the changing environment and utilize real-world
data in the most sample-efficient way. Thanks to a fast differentiable physics
simulator, the optimization for both system identification and control can be
solved efficiently for robots operating in real time. We demonstrate our method
on a set of examples in simulation and show that our results are favorable
compared to baseline methods
AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.
Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets