292,499 research outputs found
Combining Physical Simulators and Object-Based Networks for Control
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.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed
Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches
To predict a return characteristic, one may construct models of different complexity describing the dynamics of different objects. The most complex object is the entire predictive density, while the least complex is the characteristic whose forecast is of interest. This paper investigates, using experiments with real data, the relation between the complexity of the modeled object and the predictive quality of the return characteristic of interest, in the case when this characteristic is a return sign, or, equivalently, the direction-of-change. Importantly, we carry out the comparisons assuming that the underlying loss function is asymmetric, which is more plausible than the quadratic loss still prevailing in the analysis of returns. Our experiments are performed with returns of various frequencies on a stock market index and exchange rate. By and large, modeling the dynamics of returns by autoregressive conditional quantiles tends to produce forecasts of higher quality than modeling the whole predictive density or modeling the return indicators themselves.Directional prediction, sign prediction, model complexity, prediction quality, asymmetric loss, predictive density, conditional quantiles, binary autoregression
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
Understanding dynamics from visual observations is a challenging problem that
requires disentangling individual objects from the scene and learning their
interactions. While recent object-centric models can successfully decompose a
scene into objects, modeling their dynamics effectively still remains a
challenge. We address this problem by introducing SlotFormer -- a
Transformer-based autoregressive model operating on learned object-centric
representations. Given a video clip, our approach reasons over object features
to model spatio-temporal relationships and predicts accurate future object
states. In this paper, we successfully apply SlotFormer to perform video
prediction on datasets with complex object interactions. Moreover, the
unsupervised SlotFormer's dynamics model can be used to improve the performance
on supervised downstream tasks, such as Visual Question Answering (VQA), and
goal-conditioned planning. Compared to past works on dynamics modeling, our
method achieves significantly better long-term synthesis of object dynamics,
while retaining high quality visual generation. Besides, SlotFormer enables VQA
models to reason about the future without object-level labels, even
outperforming counterparts that use ground-truth annotations. Finally, we show
its ability to serve as a world model for model-based planning, which is
competitive with methods designed specifically for such tasks.Comment: Accepted by ICLR 2023. Project page: https://slotformer.github.io
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