Manipulation taxonomy

Overview

A manipulation taxonomy is a shared vocabulary for what a robot does to an object and what its hand can do. Two cards use it in different ways. T-DOM labels every step of a task with tags 2412.20998 §4. DexGrip places its gripper in a table of degrees-of-freedom (DoF) counts for six other grippers 2411.17124 tab. I. Neither card reports a downstream robot benchmark, so the taxonomies here describe designs and demonstrations, not controllers.

Variants

Deformation-aware action tags (T-DOM)

T-DOM targets objects that change shape, such as cloth, rope or foam. It has three branches: deformation (D), robot motion (M), and interactions. The interactions split into prehensile grasps, non-prehensile contacts and contact sliding 2412.20998 §4. Deformation is tagged by force type (compression, tension, torsion, shear) and by bending, which has a structured level and an unstructured level 2412.20998 §3.1 §4.1. Motion uses the kinetic, elastic, gravitational and gravitational-elastic classes §4.2. Bimanual tasks carry a separate tag set per arm §4.6. Card: T-DOM.

On ten recorded tasks, the authors report 4 action clusters with more than one member. Their own baselines, Bullock 2012 and Paulius 2020, give 21 and 22 clusters on the same actions, and the largest clusters are 10 and 17 2412.20998 §5.3. These counts come from the authors' own labels, and the paper reports no second annotator 2412.20998 §5.2.

Degrees-of-freedom count (DexGrip)

DexGrip is a soft gripper with three Fin Ray fingers, each with a belt, and a telescoping palm with a rotating suction cup. Its table lists DoF counts from 3 to 14 across six cited grippers, with DexGrip at 9 [arxiv:2411.17124 tab. I]. The 9 comes from three finger bends, three belt rotations and three palm motions 2411.17124 §III-A. The palm count includes suction, which is not a DoF in the usual sense, so the 9 is the authors' convention 2411.17124 §III-A. Card: DexGrip.

When to use which

Implementation sketch

The core of T-DOM's clustering is a grouping by equal tag tuples. Each action segment gets one tag set per arm, and actions with equal tag sets form a cluster 2412.20998 §5.2 §5.3.

# Group action segments whose full tag sets are equal  [arxiv:2412.20998 §5.3]
# Input:  actions: list of (action_id, code), code = tuple of tags per arm
# Output: clusters: list of lists of action IDs, each with more than one member

def cluster_actions(actions):
    # Equal tag sets form the groups; keep only groups with more than one member
    groups = {}
    for action_id, code in actions:
        groups.setdefault(code, []).append(action_id)
    return [ids for ids in groups.values() if len(ids) > 1]

Bimanual tasks use one tag set per arm, and a unimanual task sets the second arm to a none tag 2412.20998 §4.6 §5.2. The tag letters in the paper's table differ from its text in places, so map them before use 2412.20998 §4.2 tab. 2.

Contradictions

None known between these two cards. They count different things, a tag set and a DoF count, so their numbers are not comparable.

Papers

• DexGrip: a soft gripper with an active palm and belt surfaces, with a DoF comparison across six grippers.
• T-DOM: a tag set for deformable-object manipulation, applied by hand to ten tasks and compared with two older taxonomies.