Partial views / active perception

We discussed some of the technical challenges that come with having only partial views of the objects that you wish to manipulate in the geometric perception chapter. In the table-top manipulation setting, we can typically put cameras looking onto the table from all external angles, but our views can still be occluded in clutter.

When you're a mobile robot, we typically don't assume that you can fully instrument your environment to the same extent. Typically we try to work with only robot-mounted sensors, such as head-mounted cameras. Our antipodal grasping strategy won't work out of the box if we only see one side of each object.

The primary solution to this problem is to use machine learning. If you only see one half of a mustard bottle, then the instantaneous sensor readings simply don't contain enough information to plan a grasp; somehow we must infer something about what the back of the object looks like (aka "shape completion", though it need not be done explicitly). The reason that you would have a good guess at what the back half of the mustard bottle looks like is because you've interacted with mustard bottles before -- it is the statistics of the objects you've interacted with that provides the missing information. So learning isn't just a convenience here, it's fundamental. We'll discuss learning-based perception soon!

There is another mitigation, though, that is often available to a mobile manipulator. Now that the cameras (and all of the sensors) are mobile, we can actively move them in order to gather information / reduce uncertainty. There is a rich literature for "active perception" and "planning under uncertainty". [More to come]

Unknown (potentially dynamic) environments

For table-top manipulation, we started by assuming known objects and then discussed approaches (such as antipodal grasping) that could work on unknown objects. But throughout that discussion, we always assumed that we new the geometry and location of the table / bins! For instance, we actively cropped away the point cloud returns that hit the known environment and left only points associated with the objects. That's a very reasonable thing to do when your robot is bolted to the table, but we need to relax that assumption once we go mobile, and start interacting with more diverse / less known environments.

One could try to model everything in the environment. We could do object detection to recognize the table, and pose estimation (possibly shape estimation) to fit a parametric table model to the observations. But that's a difficult road to travel, especially in diverse and expansive scenes. Is there an analogy to the antipodal grasping for objects that can similarly reduce our need for explicit modeling of the environment?

We'd like to have a representation of the environment that does not require explicit object models, that supports the types of queries we need for motion planning (for instance, fast collision detection and minimum-distance computations), which we can update efficiently from our raw sensor data, and which scale well to large scenes. Raw, merged point clouds, for instance, are easy to update and don't require a model, but would not be ideal for fast planning queries.

Voxel grids are a geometry representation that meets (almost all of) the our desiderata -- we actually used them already in order to efficiently down-sample a point cloud. In this representation we discretize some finite volume of 3D space into a grid of fixed-size cubes; let's say 1 cm$^3.$ We mark each of these cubes as either occupied or unoccupied (or more generally, we can have a probability of being occupied). Then, for collision-free motion planning, we treat the occupied cubes as collision geometry to be avoided. Many collision queries with voxel grids can be fast (and are easily parallelized). In particular the sphere-on-voxel collision query can be very fast, which is one of the reasons that you see a number of collision geometries in the Drake models that are approximate with densely packed spheres. Figure Updating a voxel grid with points clouds is also efficient. In order to scale voxel representations to have both fine resolution and to be able to represent very large scenes, we can make use of clever data structures.

Segmenting the visual scene into objects vs environment these days is best done with machine learning; we'll implement segmentation pipelines soon.

Robot state estimation

The biggest difference that comes up in perception for a mobile manipulator is the need to estimate not only the state of the environment, but the state of the robot. For instance, in our point cloud processing algorithms so far, we have tacitly assumed that we know ${}^WX^C$, the pose of the camera in the world. We could potentially invest in elaborote calibration procedures, using known shapes and patterns, in order to estimate (offline) these camera extrinsics. We've also take the kinematics of the robot to be known -- this is a pretty reasonable assumption when the robot is bolted to the table and all of the transforms between the world and the end-effector are measured directly with high-accuracy encoders. But these assumptions break down once the robot has a mobile base.

State estimation for mobile robots has a strong history; Thrun05 is the canonical reference. Often these algorithms are based on (approximate) recursive Bayesian filtering, or the closely related smoothing algorithms Kaess08. Typically our mobile bases are instrumented with range sensors and/or cameras in addition to the wheel encoders and inertial measurement units (IMUs). The state estimation pipeline fuses these noisy measurements together into a consistent estimate. Indeed, one of the biggest lessons in mobile robots is the wheel odometry alone is almost always insufficient for estimation; real wheels slip and visual/range measurements provide an essential and independent set of measurements.

Many things have changed since Thrun05 was written. Range-based sensors (lidar and depth cameras) have improved dramatically in range, accuracy, and framerate, making the methods dramatically more effective.

Perhaps the biggest change, though, has been the shift from a heavy dependence on range-based sensors / depth-cameras towards increasingly powerful estimation algorithm which use only RGB cameras. This is due to advances in visual-inertial odometryScaramuzza11, monocular depth estimationMing21, and dense 3D reconstruction / structure from motionMildenhall21+Kerbl23, to name a few.

Wheeled robots

Even though real wheels do slip, we typically do motion planning with the no-slip assumption, then use feedback control to follow the planned path Siciliano16.

Legged robots

If your job is to do the planning and control for the legs of a robot, then there is a rich literature for you to explore. But if you are using a legged robot like Spot to perform mobile manipulation, and you're using the API provided by Boston Dynamics, then (fortunately or unfortunately) you don't get to dive into the low-level control of the legs. In fact, Spot provides an API that to first order treats the legged platform as a holonomic base. (There are secondary considerations, like the fact that the robots center of mass is limited by dynamic constraints, and may deviate from your requested trajectory in order to maintain balance).

Of course, things get much more interesting when you are performing mobile manipulation on rough or intermitted terrain; this is where the legged platform really shines!

Mapping (in addition to localization)

Identifying traversable terrain