For GQN and Arrow datasets, object shape and appearance embeddings are both 9 dimensional.

'shortcut', to presumptively output a red object when the background pixels are orange.

We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithm's strengths and weaknesses under different experimental conditions. These conditions include variations in lighting, background textures, cameras with different noise levels, and grippers. We also run analogous experiments in simulations and with real robots and present the discrepancies. Some experiments are also run in two different laboratories using same protocols to further analyze the repeatability of our results. We believe that this study, comprising 5040 experiments, provides important insights into the role and challenges of systematic experimentation in robotic manipulation, and guides the development of new algorithms by considering the factors that could impact the performance. The experiment recordings and our benchmarking software are publicly available.

We present a generative model and stochastic filtering algorithm for si- multaneous tracking of 3D position and orientation, non-rigid motion, object texture, and background texture using a single camera. We show that the solution to this problem is formally equivalent to stochastic fil- tering of conditionally Gaussian processes, a problem for which well known approaches exist [3, 8]. We propose an approach based on Monte Carlo sampling of the nonlinear component of the process (object mo- tion) and exact filtering of the object and background textures given the sampled motion. The smoothness of image sequences in time and space is exploited by using Laplace's method to generate proposal distributions for importance sampling [7]. The resulting inference algorithm encom- passes both optic flow and template-based tracking as special cases, and elucidates the conditions under which these methods are optimal.

We present a generative model and stochastic filtering algorithm for simultaneous tracking of 3D position and orientation, nonrigid motion, object texture, and background texture using a single camera. We show that the solution to this problem is formally equivalent to stochastic filtering of conditionally Gaussian processes, a problem for which well known approaches exist [3, 8]. We propose an approach based on Monte Carlo sampling of the nonlinear component of the process (object motion) and exact filtering of the object and background textures given the sampled motion. The smoothness of image sequences in time and space is exploited by using Laplace's method to generate proposal distributions for importance sampling [7]. The resulting inference algorithm encompasses both optic flow and template-based tracking as special cases, and elucidates the conditions under which these methods are optimal. We demonstrate an application of the system to 3D nonrigid face tracking.

We present a generative model and stochastic filtering algorithm for simultaneous tracking of 3D position and orientation, nonrigid motion, object texture, and background texture using a single camera. We show that the solution to this problem is formally equivalent to stochastic filtering of conditionally Gaussian processes, a problem for which well known approaches exist [3, 8]. We propose an approach based on Monte Carlo sampling of the nonlinear component of the process (object motion) and exact filtering of the object and background textures given the sampled motion. The smoothness of image sequences in time and space is exploited by using Laplace's method to generate proposal distributions for importance sampling [7]. The resulting inference algorithm encompasses both optic flow and template-based tracking as special cases, and elucidates the conditions under which these methods are optimal. We demonstrate an application of the system to 3D nonrigid face tracking.

We present a generative model and stochastic filtering algorithm for simultaneous trackingof 3D position and orientation, nonrigid motion, object texture, and background texture using a single camera. We show that the solution to this problem is formally equivalent to stochastic filtering ofconditionally Gaussian processes, a problem for which well known approaches exist [3, 8]. We propose an approach based on Monte Carlo sampling of the nonlinear component of the process (object motion) andexact filtering of the object and background textures given the sampled motion. The smoothness of image sequences in time and space is exploited by using Laplace's method to generate proposal distributions for importance sampling [7]. The resulting inference algorithm encompasses bothoptic flow and template-based tracking as special cases, and elucidates the conditions under which these methods are optimal. We demonstrate an application of the system to 3D nonrigid face tracking.