Highlights

• learning task-space, data-driven cost functions as diffusion models
• Cross-entropy or Contrastive divergence
  • try to create hard discr. regions in the modeled data -> this leads to large plateaus in the learned field with zero or noisy slope regions
• it is common to rely on task-specific samplers that first generate samples close to low cost regions (??)

Think about a cost function that involves multiple objective. Such functions are typically computed with cross-entropy or contrastive divergnce. These create hard discriminating regions in the modeled data, leading to zero gradient in the region. -> Common solution is to rely on task-specific samplers that provides samples from the low-cost regions before optimization.

Instead, the proposed method forms a smooth cost function that provides informative gradients in all the region.

To train a grasp pose generation model, they use Acronym dataset. It's a large scale dataset that contains pairs of grasp pose vs grasp success data for many different simulated objects.

Contributions

1. learning smooth cost functions (i.e., the model that can provide informative gradient in the entire region) in SE(3) as diffusion models

• Sampling from SE(3) space is not trivial. They provide some mathematical foundation for that

1. a single gradient-based optimization framework for jointly resolving grasp and motion generation

• Their method can generate the grasp and a trajectory to reach there at the same time

Background of Diffusion Models

They base their method on DSM: Denoising Score Matching, and sample from the model via Langevin MCMC.

Gaussian Distribution on SE(3) Lie Group

After massaging some equations in SE(3) Lie Group, they reperesent the proabability of a grasp $\mathbf{H} \in \text{SE}(3)$ as:

where $\mathbf{H}_\mu \in SE(3)$ is the mean and $\Sigma \in \mathbb{R}^{6\times6}$ is the covariance matrix. Logmap: $SE(3) \rightarrow \mathbb{R}^6$ projects an SE(3) pose to a 6-dim vector.

SE(3)-Diffusion Fields (SE(3)-DiF)

mapping from a query point $\mathbf{H} \in SE(3)$ to a scalar value $e \in \mathbb{R}$

where $k$ is diffusion timestep. Other parts are identical to DSM except that they work in SE(3) space.

Training SE(3)-GraspDiffusionFields

• They assume the pose of the object is known
• They learn a latent feature for every object (similar to DeepSDF: Deep Signed Distance Field)
• They jointly learn the SDF of an object and grasp diffusion model
• They don't use grasp as input, but they rather use a fixed set of N points around the grasp's center to inform the grasp pose

Setup & Architecture

1. They pre-define a fixed set of N 3D-points around the grasp's center ($\mathbf{x}_g \in \mathbb{R}^{N \times 3}$; grasp frame)
2. Given a grasp pose ($\mathbf{H}^w_g$) in world frame, they transform the points to world frame ($\mathbf{x}_w = \mathbf{H}^w_g \mathbf{x_g} \in \mathbb{R}^{N \times 3}$; world frame)
3. Then they use the pose of object m ($\mathbf{H}_w^{o_m}$) to transform the points again to the object frame ($\mathbf{x}_{o_m} = \mathbf{H}^{o_m}_w \mathbf{x}_w \in \mathbb{R}^{N \times 3}$; object frame)
4. Encoder $F_\theta$ takes in shape code $\mathbf{z}_m \in \mathbb{R}^z$ and the points in object frame $\mathbf{x}_{o_m} \in \mathbb{R}^{N \times 3}$ and output SDF prediction (scalar for each point) and additional features $\psi \in \mathbb{R}^{N \times \psi}$
5. Decoder takes both SDF prediction and features, and output energy (scalar)

TODO: I should just draw a figure by myself.

Training Objective

• $o_m$: object
• $z_m \in \mathbb{R}^z$: latent code of the object shape (SDF encoding)
• $\mathcal{L}_\text{dsm}$: Denoising Score Matching loss
  • $\mathbf{H}^w_g \in SE(3)$: grasp pose in world frame
  • $\mathbf{H}^{o_m}_{w} \in SE(3)$: object pose in world frame (during training, it's identity because it doesn't matter)
• $\mathcal{L}_\text{sdf}$: SDF prediction loss
  • $\mathbf{x}_o \in \mathbb{R}^{N \times 3}$: a random 3D point in object frame
  • $\text{sdf}_{o_m}: \mathbb{R}^3 \rightarrow \mathbb{R}^1$: Sign Distance Field

Grasp and motion generation

They try to find the minimum cost trajectory $\tau^*$:

• $c_j(\tau)$: a cost function
• $w_j > 0$: weights

The grasp generation model (SE(3)-DiF) forms one of the cost functions. With that, this optimization can take into account grasping pose.

:::message Cost function with SE(3)-DiF

, where $\phi_{ee}: \mathbb{R}^{d_q} \rightarrow \text{SE}(3)$ is forward kinematics model. :::

Intuitively, this cost function provides low cost to those robot configurations that lead to good grasps.

How to solve the minimization / optimization problem? --> Diffusion reverse process! Note that this does NOT involve any training, this is simply the reverse process of diffusion.

1. Define the distribution over trajectories as $q(\mathbf{\tau}|k) \propto \exp(- \mathcal{J}(\mathbf{\tau}, k))$ (see Planning as Inference)

• This $q(\mathbf{\tau}|k)$ will be the target distribution

1. Run reverse diffusion process: Langevin diffusion process

A small caveat: all cost function must be differentiable to compute $\nabla_{\tau_k} c_{n_s}$

Highlights from experiment section

TODO

Scratch

Smooth function --> the cost function exposing informative gradients in the entire space. They learn smooth cost functions in the SE(3) robot's workspace -> task-specific costs.

Acronym dataset

The resulting models allow to move samples to the low-cost regions via inverse diffusion process.

Combining their learned diffusion model for 6D grasp pose generation with other smooth costs (trajectory smoothness, collision avoidance cost).

Contributions

1. learning smooth cost functions in SE(3) as diffusion models
2. A single gradient-based optimization framework for grasp and motion generation

Experiments

Three tasks

• Picking with occlusions
• Picking and reorienting an object
• pick and place in shelves

Terms

• tangent space tangent space at the identity is called Lie algebra.
• logmap (SE(3) -> R^6) and expmap (R^6 -> SE(3))

Q & A

• what is the main goal of that paper?
• what data format they use as input?
• what assumptions do they make (object centric/dense features)?
• What different assumption do we want to make?
• Is it right/wrong/preferable?
• What did they show in figure 1?
• What did they show in figure 2?
• What did they show in exp 1?
• How did they show it?
• Compared to what?
• What is the real world demo?
• Why is this demo difficult? (they did 6 DOF)