Ab Initio Particle-based Object Manipulation

This paper presents Particle-based Object Manipulation (Prompt), a new approach to robot manipulation of novel objects ab initio, without prior object models or pre-training on a large object data set. The key element of Prompt is a particle-based object representation, in which each particle represents a point in the object, the local geometric, physical, and other features of the point, and also its relation with other particles. Like the model-based analytic approaches to manipulation, the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions. Like the data-driven approaches, the particle representation is learned online in real-time from visual sensor input, specifically, multi-view RGB images. The particle representation thus connects visual perception with robot control. Prompt combines the benefits of both model-based reasoning and data-driven learning. We show empirically that Prompt successfully handles a variety of everyday objects, some of which are transparent. It handles various manipulation tasks, including grasping, pushing, etc,. Our experiments also show that Prompt outperforms a state-of-the-art data-driven grasping method on the daily objects, even though it does not use any offline training data.

FineAction: A Fined Video Dataset for Temporal Action Localization

On the existing benchmark datasets, THUMOS14 and ActivityNet, temporal action localization techniques have achieved great success. However, there are still existing some problems, such as the source of the action is too single, there are only sports categories in THUMOS14, coarse instances with uncertain boundaries in ActivityNet and HACS Segments interfering with proposal generation and behavior prediction. To take temporal action localization to a new level, we develop FineAction, a new large-scale fined video dataset collected from existing video datasets and web videos. Overall, this dataset contains 139K fined action instances densely annotated in almost 17K untrimmed videos spanning 106 action categories. FineAction has a more fined definition of action categories and high-quality annotations to reduce the boundary uncertainty compared to the existing action localization datasets. We systematically investigate representative methods of temporal action localization on our dataset and obtain some interesting findings with further analysis. Experimental results reveal that our FineAction brings new challenges for action localization on fined and multi-label instances with shorter duration. This dataset will be public in the future and we hope our FineAction could advance research towards temporal action localization. Our dataset website is at https://deeperaction.github.io/fineaction/.

Learning Latent Graph Dynamics for Deformable Object Manipulation

Manipulating deformable objects, such as cloth and ropes, is a long-standing challenge in robotics: their large number of degrees of freedom (DoFs) and complex non-linear dynamics make motion planning extremely difficult. This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). To tackle the challenge of many DoFs and complex dynamics, G-DOOM approximates a deformable object as a sparse set of interacting keypoints and learns a graph neural network that captures abstractly the geometry and interaction dynamics of the keypoints. Further, to tackle the perceptual challenge, specifically, object self-occlusion, G-DOOM adds a recurrent neural network to track the keypoints over time and condition their interactions on the history. We then train the resulting recurrent graph dynamics model through contrastive learning in a high-fidelity simulator. For manipulation planning, G-DOOM explicitly reasons about the learned dynamics model through model-predictive control applied at each of the keypoints. We evaluate G-DOOM on a set of challenging cloth and rope manipulation tasks and show that G-DOOM outperforms a state-of-the-art method. Further, although trained entirely on simulation data, G-DOOM transfers directly to a real robot for both cloth and rope manipulation in our experiments.

HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification

Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems. Learning discriminative features for Re-ID remains a challenge due to the large variations in the image space, e.g., continuously changing human poses, illuminations and point of views. In this paper, we propose HAVANA, a novel extensible, light-weight HierArchical and VAriation-Normalized Autoencoder that learns features robust to intra-class variations. In contrast to existing generative approaches that prune the variations with heavy extra supervised signals, HAVANA suppresses the intra-class variations with a Variation-Normalized Autoencoder trained with no additional supervision. We also introduce a novel Jensen-Shannon triplet loss for contrastive distribution learning in Re-ID. In addition, we present Hierarchical Variation Distiller, a hierarchical VAE to factorize the latent representation and explicitly model the variations. To the best of our knowledge, HAVANA is the first VAE-based framework for person ReID.

Towards Overcoming False Positives in Visual Relationship Detection

In this paper, we investigate the cause of the high false positive rate in Visual Relationship Detection (VRD). We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e.g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals. This paper presents Spatially-Aware Balanced negative pRoposal sAmpling (SABRA), a robust VRD framework that alleviates the influence of false positives. To effectively optimize the model under imbalanced distribution, SABRA adopts Balanced Negative Proposal Sampling (BNPS) strategy for mini-batch sampling. BNPS divides proposals into 5 well defined sub-classes and generates a balanced training distribution according to the inverse frequency. BNPS gives an easier optimization landscape and significantly reduces the number of false positives. To further resolve the low-frequency challenging false positive proposals with high spatial ambiguity, we improve the spatial modeling ability of SABRA on two aspects: a simple and efficient multi-head heterogeneous graph attention network (MH-GAT) that models the global spatial interactions of objects, and a spatial mask decoder that learns the local spatial configuration. SABRA outperforms SOTA methods by a large margin on two human-object interaction (HOI) datasets and one general VRD dataset.

Hierarchical Federated Learning through LAN-WAN Orchestration

Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL protocols has a critical communication bottleneck in a federated network coupled with privacy concerns, usually powered by a wide-area network (WAN). Such a WAN-driven FL design leads to significantly high cost and much slower model convergence. In this work, we propose an efficient FL protocol, which involves a hierarchical aggregation mechanism in the local-area network (LAN) due to its abundant bandwidth and almost negligible monetary cost than WAN. Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN. We further design a concrete FL platform, namely LanFL, that incorporates several key techniques to handle those challenges introduced by LAN: cloud-device aggregation architecture, intra-LAN peer-to-peer (p2p) topology generation, inter-LAN bandwidth capacity heterogeneity. We evaluate LanFL on 2 typical Non-IID datasets, which reveals that LanFL can significantly accelerate FL training (1.5x-6.0x), save WAN traffic (18.3x-75.6x), and reduce monetary cost (3.8x-27.2x) while preserving the model accuracy.

Contrastive Variational Model-Based Reinforcement Learning for Complex Observations

Deep model-based reinforcement learning (MBRL) has achieved great sample-efficiency and generalization in decision making for sophisticated simulated tasks, such as Atari games. However, real-world robot decision making requires reasoning with complex natural visual observations. This paper presents Contrastive Variational Reinforcement Learning (CVRL), an MBRL framework for complex natural observations. In contrast to the commonly used generative world models, CVRL learns a contrastive variational world model by maximizing the mutual information between latent states and observations discriminatively by contrastive learning. Contrastive learning avoids modeling the complex observation space and is significantly more robust than the standard generative world models. For decision making, CVRL discovers long-horizon behavior by online search guided by an actor-critic. CVRL achieves comparable performance with the state-of-the-art (SOTA) generative MBRL approaches on a series of Mujoco tasks, and significantly outperforms SOTAs on Natural Mujoco tasks, a new, more challenging continuous control RL benchmark with complex observations introduced in this paper.

Balanced Meta-Softmax for Long-Tailed Visual Recognition

Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distributions. In this paper, we show that Softmax function, though used in most classification tasks, gives a biased gradient estimation under the long-tailed setup. This paper presents Balanced Softmax, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing. Theoretically, we derive the generalization bound for multiclass Softmax regression and show our loss minimizes the bound. In addition, we introduce Balanced Meta-Softmax, applying a complementary Meta Sampler to estimate the optimal class sample rate and further improve long-tailed learning. In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.

DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space

It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accepted benchmark. In this work, we propose a new light-weight benchmark task called Diner Dash for evaluating the performance in a complicated task with high dimensional action space. In contrast to the traditional Atari games that only have a flat structure of goals and very few actions, the proposed benchmark task has a hierarchical task structure and size of 57 for the action space and hence can facilitate the development of policy learning in complicated tasks. On top of that, we introduce Decomposed Policy Graph Modelling (DPGM), an algorithm that combines both graph modelling and deep learning to allow explicit domain knowledge embedding and achieves significant improvement comparing to the baseline. In the experiments, we have shown the effectiveness of the domain knowledge injection via a specially designed imitation algorithm as well as results of other popular algorithms.

Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction

Understanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. This is challenging because it requires effectively modeling the socially aware crowd spatial interaction and complex temporal dependencies. We believe attention is the most important factor for trajectory prediction. In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms. STAR models intra-graph crowd interaction by TGConv, a novel Transformer-based graph convolution mechanism. The inter-graph temporal dependencies are modeled by separate temporal Transformers. STAR captures complex spatio-temporal interactions by interleaving between spatial and temporal Transformers. To calibrate the temporal prediction for the long-lasting effect of disappeared pedestrians, we introduce a read-writable external memory module, consistently being updated by the temporal Transformer. We show STAR outperforms the state-of-the-art models on 4 out of 5 real-world pedestrian trajectory prediction datasets, and achieves comparable performance on the rest.