(By Li Yang Ku)
On this publish I’ll briefly undergo the issue of Activity and Movement Planning (TAMP) and discuss some current works that attempt to sort out it. One of many predominant motivation of fixing the TAMP downside is to permit robots to resolve family duties just like the robotic Rosey within the cartoon Jetsons. We’re speaking about extra “difficult” duties resembling grabbing a mug in the back of your shelf which most Roombas would simply surrender. Activity like these can’t normally be achieved in a single movement and the robotic would possibly want to maneuver issues round earlier than with the ability to attain the goal. Activity and Movement Planning (TAMP) is referring to this set of duties that requires a number of sequences of deliberate motions.
Historically, the AI neighborhood centered extra on symbolic activity planners that makes use of first order logic to reply questions. It was as soon as thought that by combining all these logic, a machine could be as clever as people. The truth is, an entire business of knowledgeable system was as soon as constructed primarily based on these assumptions.
(which jogs my memory of the present self-driving automotive business.) Then again, movement planning is a robotics downside and plenty of approaches that search a path within the robotic joint house primarily based on heuristics had been invented. Each fields have first rate options to the person issues however merely combining them collectively received’t resolve the TAMP downside. One of many purpose is as a result of a activity planner with out the information of whether or not the robotic can attain an object can not plan symbolically.
Its arduous to say who or when the identify TAMP was coined, since it isn’t an issue arduous to find however an issue that stands out if the aim is to construct a robotic that solves duties. The precise phrases nonetheless appears to first seem within the title of the paper “Hierarchical Activity and Movement Planning within the Now”  revealed in 2011 by Leslie Kaelbling and Tomas Perez, the MIT twin that had been most well-known for his or her work on planning in partially observable environments. On this paper, a aim is iteratively divided into subgoals primarily based on the precondition of this aim. For instance, to put object O at area R, two precondition is required 1) the robotic must be holding O, and a pair of) the trail to R must be cleared. The aim is split till we attain a leaf aim which then the movement planning is ran and the robotic executes the motion. Preconditions for objectives are labeled with abstraction ranges to assist decide which pre-condition must be thought of to separate into subgoals first. As soon as the robotic executes the motion, the entire planning course of is processed once more. This strategy was examined on a 2D robotic in simulation on family duties. The principle limitation of this strategy appears to be requiring actions to have nicely outlined precondtions.
Within the paper “Mixed Activity and Movement Planning By way of an Extensible Planner-Impartial Interface Layer”  revealed in 2014, Srivastava et al. proposed an interface between off the shelf activity planner and movement planner. One of many predominant difficulties on combining activity and movement planning is to bridge the symbolic house utilized in activity planning with the continual house utilized in movement planning. On this work, pose mills are used to create a finite set of pose references that can be utilized in activity planning whereas the movement planner makes use of these generated poses to plan trajectories. The interface additional updates the duty planner when there aren’t any legitimate trajectories. The authors demonstrated this strategy on a activity which the robotic PR2 has to understand a goal object on a densely cluttered desk. The picture beneath exhibits PR2 making an attempt to understand the gray object amongst a pile of blocking objects.
Earlier than we go additional we have to first introduce the Planning Area Definition Language (PDDL). PDDL was invented in 1998 to offer a platform for the Worldwide Planning Competitors. It was impressed by two earlier language (STRIPS, ADL) designed for robotics planning that’s extra motion centered. Actions are outlined with pre-conditions and post-conditions and a planning downside is include area descriptions and downside descriptions. The area description describes the surroundings whereas the issue description states what aim the planner ought to attempt to attain. The aim is to discover a sequence of actions described within the area description to fulfill the issue description. The duty planner we described within the earlier paper  is a planner within the PDDL area. Since PDDL 2.1, fluents had been launched to the language. Fluents are numerical valued variables within the surroundings that may be modified by actions. (Fluents shall be talked about within the final paper of this publish.) Quite a lot of extensions have been proposed because the invention of PDDL, resembling Probabilistic PDDL (PPDDL) that takes into consideration uncertainty.
Leaping again to 2020, I’m going to speak about two associated current papers tackling the TAMP downside. The primary paper “PDDLStream: Integrating Symbolic Planners and Blackbox Samplers by way of Optimistic Adaptive Planning”  by Garret et al. is a publication from the identical MIT group managed by Leslie Kaelbling and Tomas Perez. This may be thought of a PDDL extension and the identify is predicated on the brand new added conditional generator referred to as “Stream”, which might generate a sequence of outputs that fulfill circumstances. An instance of a stream is a grasps stream that generates sequences of grasp poses given an object pose. One of many predominant variations between this strategy and the earlier paper  by Srivastava et al. is that in  the authors attempt to bridge PDDL activity planning and movement planning via an interface, whereas in  a brand new language is proposed to wrap off the shelf PDDL activity planners as a subroutine. The authors of the PDDLStream paper argues that their strategy gives a domain-agnostic language which might resolve issues of various domains (resembling arm robotic versus multi-robot rovers) with out redesigning the interface. The way in which PDDLStream works is that at every iteration sure quantities of recent info are generated primarily based on the streams given current info, these new info together with current info and the area info are then fed right into a PDDL solver that tries to discover a answer. This course of iterates till we’ve got an answer. To cut back the search time, ranges of info are launched to explain what number of stream evaluations is required to certify a reality. This info is used to information the stream technology course of to forestall going for extra difficult options first. The authors additionally included a couple of variants that delay the analysis of sure expensive streams to hurry up the search course of which I can’t go into particulars on this publish.
The final paper I’m going to speak about on this publish is what first obtained my consideration. This paper “On-line Replanning in Perception House for Partially Observable Activity and Movement Issues” additionally by Garret et al. was primarily based on the earlier PDDLStream paper  and is a collaboration between the MIT group and Dieter Fox, who’s one in every of my favourite robotics professors and now a director at robotics analysis in Nvidia (Nvidia arrange an entire department in Seattle for him so I suppose they love him extra.) This paper was additionally marketed by Josh Tenenbaum in his keynote speak at RSS 2020. The distinction versus  is that this work is making an attempt to resolve a set of partially observable TAMP issues referred to as “Stochastic Shortest Path Issues” (SSPP). SSPP is a kind of perception house planning issues, which as a substitute of planning over states, planning are accomplished over distribution of states with a purpose to deal with stochasticity. An instance of a perception house planning downside could be stating the present state of an object to be 60% behind field A and 40% behind field B and the aim is to execute a sequence of actions such that the item could be 95% behind field C. This appears a reasonably easy instance, however it may well get difficult if say the room is darkish and there’s a further choice to go and activate the sunshine so that you’d be much less more likely to combine up the item with the same wanting one. To unravel a probabilistic SSPP downside with a deterministic PDDLStream solver, the authors applied a particle-based perception illustration (its a particle filter.) A particle represents an object pose related to a likelihood of this pose being the true pose. These particles are represented by fluents, that are variables that may be modified by actions within the PDDL language. By having an update-belief stream that certifies legitimate perception updates and a detect motion that might lead to distributions over poses being up to date primarily based on Bayesian replace, probabilistic representations are symbolized and change into solvable by a deterministic solver. For instance, with a purpose to discover an object that’s not seen, the robotic could take away the item that it thinks probably blocking the item. It will than do a detect motion which the remark would replace current particles that signify the poses of the item. Within the left picture above, every cross or asterisk represents a particle that represents a potential pose of the inexperienced block. Inexperienced marks represents larger likelihood whereas black represents decrease likelihood. There may be uncertainty of the situation as a result of the robotic is viewing from an angle which the inexperienced field is just not seen. To make the entire system work, replanning that follows earlier plans and methods to defer heavy computations are additionally launched within the work, which I can’t dive in. The authors examined this framework in simulation and in addition demonstrated on a robotic in a kitchen surroundings which you’ll be able to see movies right here.
In abstract, we’ve got some good progress in fixing TAMP. My solely complain could be that these approaches appear to be primarily based on conventional frameworks of activity planning and movement planning. TAMP is a tough downside so constructing on current approaches makes the issue extra manageable. Nevertheless, if we do have sufficient useful resource, it might be fascinating to re-imagine a framework that doesn’t separate activity and movement planning however deal with them as the identical sort of downside however beneath completely different scales.